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ADVANCED DETERMINANT CALCULUS
C. KRATTENTHALER
†
Institut f¨ ur Mathematik der Universit¨ at Wien,
Strudlhofgasse 4, A-1090 Wien, Austria.
E-mail: kratt@pap.univie.ac.at
WWW: http://radon.mat.univie.ac.at/People/kratt
Dedicated to the pioneer of determinant evaluations (among many other things),
George Andrews
Abstract. The purpose of this article is threefold. First, it provides the reader with
a few useful and eﬃcient tools which should enable her/him to evaluate nontrivial de-
terminants for the case such a determinant should appear in her/his research. Second,
it lists a number of such determinants that have been already evaluated, together with
explanations which tell in which contexts they have appeared. Third, it points out
references where further such determinant evaluations can be found.
1. Introduction
Imagine, you are working on a problem. As things develop it turns out that, in
order to solve your problem, you need to evaluate a certain determinant. Maybe your
determinant is
det
1≤i,j,≤n

. (1.4)
Honestly, which ideas would you have? (Just to tell you that I do not ask for something
impossible: Each of these four determinants can be evaluated in “closed form”. If you
want to see the solutions immediately, plus information where these determinants come
from, then go to (2.7), (2.17)/(3.12), (2.19)/(3.30), respectively (3.47).)
Okay, let us try some row and column manipulations. Indeed, although it is not
completely trivial (actually, it is quite a challenge), that would work for the ﬁrst two
determinants, (1.1) and (1.2), although I do not recommend that. However, I do not
recommend at all that you try this with the latter two determinants, (1.3) and (1.4). I
promise that you will fail. (The determinant (1.3) does not look much more complicated
than (1.2). Yet, it is.)
So, what should we do instead?
Of course, let us look in the literature! Excellent idea. We may have the problem
of not knowing where to start looking. Good starting points are certainly classics like
[119], [120], [121], [127] and [178]
1
. This will lead to the ﬁrst success, as (1.1) does
indeed turn up there (see [119, vol. III, p. 311]). Yes, you will also ﬁnd evaluations for
(1.2) (see e.g. [126]) and (1.3) (see [112, Theorem 7]) in the existing literature. But at
the time of the writing you will not, to the best of my knowledge, ﬁnd an evaluation of
(1.4) in the literature.
The purpose of this article is threefold. First, I want to describe a few useful and
eﬃcient tools which should enable you to evaluate nontrivial determinants (see Sec-
tion 2). Second, I provide a list containing a number of such determinants that have
been already evaluated, together with explanations which tell in which contexts they
have appeared (see Section 3). Third, even if you should not ﬁnd your determinant
in this list, I point out references where further such determinant evaluations can be
found, maybe your determinant is there.
Most important of all is that I want to convince you that, today,
Evaluating determinants is not (okay: may not be) diﬃcult!
When George Andrews, who must be rightly called the pioneer of determinant evalua-
tions, in the seventies astounded the combinatorial community by his highly nontrivial
determinant evaluations (solving diﬃcult enumeration problems on plane partitions),
it was really diﬃcult. His method (see Section 2.6 for a description) required a good
“guesser” and an excellent “hypergeometer” (both of which he was and is). While at
that time especially to be the latter was quite a task, in the meantime both guessing and
evaluating binomial and hypergeometric sums has been largely trivialized, as both can
be done (most of the time) completely automatically. For guessing (see Appendix A)
1
Turnbull’s book [178] does in fact contain rather lots of very general identities satisﬁed by determi-
nants, than determinant “evaluations” in the strict sense of the word. However, suitable specializations
of these general identities do also yield “genuine” evaluations, see for example Appendix B. Since the
value of this book may not be easy to appreciate because of heavy notation, we refer the reader to
[102] for a clariﬁcation of the notation and a clear presentation of many such identities.
ADVANCED DETERMINANT CALCULUS 3
this is due to tools like Superseeker
2
, gfun and Mgfun
3
[152, 24], and Rate
4
(which is
by far the most primitive of the three, but it is the most eﬀective in this context). For
“hypergeometrics” this is due to the “WZ-machinery”
5
(see [130, 190, 194, 195, 196]).
And even if you should meet a case where the WZ-machinery should exhaust your com-
puter’s capacity, then there are still computer algebra packages like HYP and HYPQ
6
,
or HYPERG
7
, which make you an expert hypergeometer, as these packages comprise
large parts of the present hypergeometric knowledge, and, thus, enable you to con-
veniently manipulate binomial and hypergeometric series (which George Andrews did
largely by hand) on the computer. Moreover, as of today, there are a few new (perhaps
just overlooked) insights which make life easier in many cases. It is these which form
large parts of Section 2.
So, if you see a determinant, don’t be frightened, evaluate it yourself!
2. Methods for the evaluation of determinants
In this section I describe a few useful methods and theorems which (may) help you
to evaluate a determinant. As was mentioned already in the Introduction, it is always
possible that simple-minded things like doing some row and/or column operations, or
applying Laplace expansion may produce an (usually inductive) evaluation of a deter-
minant. Therefore, you are of course advised to try such things ﬁrst. What I am
mainly addressing here, though, is the case where that ﬁrst, “simple-minded” attempt
failed. (Clearly, there is no point in addressing row and column operations, or Laplace
expansion.)
Yet, we must of course start (in Section 2.1) with some standard determinants, such
as the Vandermonde determinant or Cauchy’s double alternant. These are of course
well-known.
In Section 2.2 we continue with some general determinant evaluations that generalize
the evaluation of the Vandermonde determinant, which are however apparently not
equally well-known, although they should be. In fact, I claim that about 80 % of the
determinants that you meet in “real life,” and which can apparently be evaluated, are a
special case of just the very ﬁrst of these (Lemma 3; see in particular Theorem 26 and
the subsequent remarks). Moreover, as is demonstrated in Section 2.2, it is pure routine
to check whether a determinant is a special case of one of these general determinants.
Thus, it can be really considered as a “method” to see if a determinant can be evaluated
by one of the theorems in Section 2.2.
2
the electronic version of the “Encyclopedia of Integer Sequences” [162, 161], written and developed
by Neil Sloane and Simon Plouﬀe; see http://www.research.att.com/~njas/sequences/ol.html
3
written by Bruno Salvy and Paul Zimmermann, respectively Frederic Chyzak; available from
http://pauillac.inria.fr/algo/libraries/libraries.html
4
written in Mathematica by the author; available fromhttp://radon.mat.univie.ac.at/People/kratt;
the Maple equivalent GUESS by Fran¸ cois B´eraud and Bruno Gauthier is available from
http://www-igm.univ-mlv.fr/~gauthier
5
Maple implementations written by Doron Zeilberger are available from
http://www.math.temple.edu/~zeilberg, Mathematica implementations written by
Peter Paule, Axel Riese, Markus Schorn, Kurt Wegschaider are available from
http://www.risc.uni-linz.ac.at/research/combinat/risc/software
6
written in Mathematica by the author; available fromhttp://radon.mat.univie.ac.at/People/kratt
7
written in Maple by Bruno Ghauthier; available from http://www-igm.univ-mlv.fr/~gauthier
4 C. KRATTENTHALER
The next method which I describe is the so-called “condensation method” (see Sec-
tion 2.3), a method which allows to evaluate a determinant inductively (if the method
works).
In Section 2.4, a method, which I call the “identiﬁcation of factors” method, is de-
scribed. This method has been extremely successful recently. It is based on a very
simple idea, which comes from one of the standard proofs of the Vandermonde deter-
minant evaluation (which is therefore described in Section 2.1).
The subject of Section 2.5 is a method which is based on ﬁnding one or more diﬀeren-
tial or diﬀerence equations for the matrix of which the determinant is to be evaluated.
Section 2.6 contains a short description of George Andrews’ favourite method, which
basically consists of explicitly doing the LU-factorization of the matrix of which the
determinant is to be evaluated.
The remaining subsections in this section are conceived as a complement to the pre-
ceding. In Section 2.7 a special type of determinants is addressed, Hankel determinants.
(These are determinants of the form det
1≤i,j≤n
(a
i+j
), and are sometimes also called per-
symmetric or Tur´ anian determinants.) As is explained there, you should expect that a
Hankel determinant evaluation is to be found in the domain of orthogonal polynomials
and continued fractions. Eventually, in Section 2.8 a few further, possibly useful results
are exhibited.
Before we ﬁnally move into the subject, it must be pointed out that the methods
of determinant evaluation as presented here are ordered according to the conditions a
determinant must satisfy so that the method can be applied to it, from “stringent” to
“less stringent”. I. e., ﬁrst come the methods which require that the matrix of which
the determinant is to be taken satisﬁes a lot of conditions (usually: it contains a lot of
parameters, at least, implicitly), and in the end comes the method (LU-factorization)
which requires nothing. In fact, this order (of methods) is also the order in which I
recommend that you try them on your determinant. That is, what I suggest is (and
this is the rule I follow):
(0) First try some simple-minded things (row and column operations, Laplace expan-
sion). Do not waste too much time. If you encounter a Hankel-determinant then
see Section 2.7.
(1) If that fails, check whether your determinant is a special case of one of the general
determinants in Sections 2.2 (and 2.1).
(2) If that fails, see if the condensation method (see Section 2.3) works. (If necessary,
try to introduce more parameters into your determinant.)
(3) If that fails, try the “identiﬁcation of factors” method (see Section 2.4). Alterna-
tively, and in particular if your matrix of which you want to ﬁnd the determinant
is the matrix deﬁning a system of diﬀerential or diﬀerence equations, try the dif-
ferential/diﬀerence equation method of Section 2.5. (If necessary, try to introduce
a parameter into your determinant.)
(4) If that fails, try to work out the LU-factorization of your determinant (see Sec-
tion 2.6).
(5) If all that fails, then we are really in trouble. Perhaps you have to put more eﬀorts
into determinant manipulations (see suggestion (0))? Sometimes it is worthwile
to interpret the matrix whose determinant you want to know as a linear map and
try to ﬁnd a basis on which this map acts triangularly, or even diagonally (this
ADVANCED DETERMINANT CALCULUS 5
requires that the eigenvalues of the matrix are “nice”; see [47, 48, 84, 93, 192] for
examples where that worked). Otherwise, maybe something from Sections 2.8 or
3 helps?
A ﬁnal remark: It was indicated that some of the methods require that your deter-
minant contains (more or less) parameters. Therefore it is always a good idea to:
Introduce more parameters into your determinant!
(We address this in more detail in the last paragraph of Section 2.1.) The more param-
eters you can play with, the more likely you will be able to carry out the determinant
evaluation. (Just to mention a few examples: The condensation method needs, at least,
two parameters. The “identiﬁcation of factors” method needs, at least, one parameter,
as well as the diﬀerential/diﬀerence equation method in Section 2.5.)
2.1. A few standard determinants. Let us begin with a short proof of the Van-
dermonde determinant evaluation
det
1≤i,j≤n

X
j−1
i

=
¸
1≤i<j≤n
(X
j
−X
i
). (2.1)
Although the following proof is well-known, it makes still sense to quickly go through
it because, by extracting the essence of it, we will be able to build a very powerful
method out of it (see Section 2.4).
If X
i
1
= X
i
2
with i
1
= i
2
, then the Vandermonde determinant (2.1) certainly vanishes
because in that case two rows of the determinant are identical. Hence, (X
i
1
− X
i
2
)
divides the determinant as a polynomial in the X
i
’s. But that means that the complete
product
¸
1≤i<j≤n
(X
j
−X
i
) (which is exactly the right-hand side of (2.1)) must divide
the determinant.
On the other hand, the determinant is a polynomial in the X
i
’s of degree at most

n
2

. Combined with the previous observation, this implies that the determinant equals
the right-hand side product times, possibly, some constant. To compute the constant,
compare coeﬃcients of X
0
1
X
1
2
X
n−1
n
on both sides of (2.1). This completes the proof
of (2.1).
At this point, let us extract the essence of this proof as we will come back to it in
Section 2.4. The basic steps are:
1. Identiﬁcation of factors
2. Determination of degree bound
3. Computation of the multiplicative constant.
An immediate generalization of the Vandermonde determinant evaluation is given by
the proposition below. It can be proved in just the same way as the above proof of the
Vandermonde determinant evaluation itself.
Proposition 1. Let X
1
, X
2
, . . . , X
n
be indeterminates. If p
1
, p
2
, . . . , p
n
are polynomials
of the form p
j
(x) = a
j
x
j−1
+ lower terms, then
det
1≤i,j≤n
(p
j
(X
i
)) = a
1
a
2
a
n
¸
1≤i<j≤n
(X
j
−X
i
). (2.2)
6 C. KRATTENTHALER
The following variations of the Vandermonde determinant evaluation are equally easy
to prove.
Lemma 2. The following identities hold true:
det
1≤i,j≤n
(X
j
i
−X
−j
i
) = (X
1
X
n
)
−n
¸
1≤i<j≤n

n
¸
i=1
(X
i
+ 1). (2.6)
We remark that the evaluations (2.3), (2.4), (2.5) are basically the Weyl denominator
factorizations of types C, B, D, respectively (cf. [52, Lemma 24.3, Ex. A.52, Ex. A.62,
Ex. A.66]). For that reason they may be called the “symplectic”, the “odd orthogonal”,
and the “even orthogonal” Vandermonde determinant evaluation, respectively.
If you encounter generalizations of such determinants of the form det
1≤i,j≤n
(x
λ
j
i
)
or det
1≤i,j≤n
(x
λ
j
i
− x
−λ
j
i
), etc., then you should be aware that what you encounter is
basically Schur functions, characters for the symplectic groups, or characters for the
orthogonal groups (consult [52, 105, 137] for more information on these matters; see
in particular [105, Ch. I, (3.1)], [52, p. 403, (A.4)], [52, (24.18)], [52, (24.40) + ﬁrst
paragraph on p. 411], [137, Appendix A2], [52, (24.28)]). In this context, one has to
also mention Okada’s general results on evaluations of determinants and Pfaﬃans (see
Section 2.8 for deﬁnition) in [124, Sec. 4] and [125, Sec. 5].
Another standard determinant evaluation is the evaluation of Cauchy’s double alter-
nant (see [119, vol. III, p. 311]),
det
1≤i,j≤n

1
X
i
+Y
j

=
¸
1≤i<j≤n
(X
i
−X
j
)(Y
i
−Y
j
)
¸
1≤i,j≤n
(X
i
+Y
j
)
. (2.7)
Once you have seen the above proof of the Vandermonde determinant evaluation, you
will immediately know how to prove this determinant evaluation.
On setting X
i
= i and Y
i
= i, i = 1, 2, . . . , n, in (2.7), we obtain the evaluation of our
ﬁrst determinant in the Introduction, (1.1). For the evaluation of a mixture of Cauchy’s
double alternant and Vandermonde’s determinant see [15, Lemma 2].
ADVANCED DETERMINANT CALCULUS 7
Whether or not you tried to evaluate (1.1) directly, here is an important lesson to be
learned (it was already mentioned earlier): To evaluate (1.1) directly is quite diﬃcult,
whereas proving its generalization (2.7) is almost completely trivial. Therefore, it is
always a good idea to try to introduce more parameters into your determinant. (That is,
in a way such that the more general determinant still evaluates nicely.) More parameters
mean that you have more objects at your disposal to play with.
The most stupid way to introduce parameters is to just write X
i
instead of the row
index i, or write Y
j
instead of the column index j.
8
For the determinant (1.1) even
both simultaneously was possible. For the determinant (1.2) either of the two (but not
both) would work. On the contrary, there seems to be no nontrivial way to introduce
more parameters in the determinant (1.4). This is an indication that the evaluation of
this determinant is in a diﬀerent category of diﬃculty of evaluation. (Also (1.3) belongs
to this “diﬀerent category”. It is possible to introduce one more parameter, see (3.32),
but it does not seem to be possible to introduce more.)
2.2. A general determinant lemma, plus variations and generalizations.
In this section I present an apparently not so well-known determinant evaluation that
generalizes Vandermonde’s determinant, and some companions. As Lascoux pointed
out to me, most of these determinant evaluations can be derived from the evaluation
of a certain determinant of minors of a given matrix due to Turnbull [179, p. 505], see
Appendix B. However, this (these) determinant evaluation(s) deserve(s) to be better
known. Apart from the fact that there are numerous applications of it (them) which I
am aware of, my proof is that I meet very often people who stumble across a special
case of this (these) determinant evaluation(s), and then have a hard time to actually
do the evaluation because, usually, their special case does not show the hidden general
structure which is lurking behind. On the other hand, as I will demonstrate in a mo-
ment, if you know this (these) determinant evaluation(s) then it is a matter completely
mechanical in nature to see whether it (they) is (are) applicable to your determinant
or not. If one of them is applicable, you are immediately done.
The determinant evaluation of which I am talking is the determinant lemma from
[85, Lemma 2.2] given below. Here, and in the following, empty products (like (X
i
+
A
n
)(X
i
+A
n−1
) (X
i
+A
j+1
) for j = n) equal 1 by convention.
Lemma 3. Let X
1
, . . . , X
n
, A
2
, . . . , A
n
, and B
2
, . . . , B
n
be indeterminates. Then there
holds
det
1≤i,j≤n

=
¸
1≤i<j≤n
(X
i
−X
j
)
¸
2≤i≤j≤n
(B
i
−A
j
). (2.8)
8
Other common examples of introducing more parameters are: Given that the (i, j)-entry of your
determinant is a binomial such as

i+j
2i−j

, try

x+i+j
2i−j

(that works; see (3.30)), or even

x+y+i+j
y+2i−j

(that
does not work; but see (1.2)), or

x+i+j
2i−j

+

y+i+j
2i−j

(that works; see (3.32), and consult Lemma 19
and the remarks thereafter). However, sometimes parameters have to be introduced in an unexpected
way, see (3.49). (The parameter x was introduced into a determinant of Bombieri, Hunt and van der
Poorten, which is obtained by setting x = 0 in (3.49).)
8 C. KRATTENTHALER
Once you have guessed such a formula, it is easily proved. In the proof in [85] the
determinant is reduced to a determinant of the form (2.2) by suitable column operations.
Another proof, discovered by Amdeberhan (private communication), is by condensation,
see Section 2.3. For a derivation from the above mentioned evaluation of a determinant
of minors of a given matrix, due to Turnbull, see Appendix B.
Now let us see what the value of this formula is, by checking if it is of any use in the
case of the second determinant in the Introduction, (1.2). The recipe that you should
follow is:
1. Take as many factors out of rows and/or columns of your determinant, so that all
denominators are cleared.
2. Compare your result with the determinant in (2.8). If it matches, you have found
the evaluation of your determinant.
Okay, let us do so:
det
1≤i,j≤n

.
Now compare with the determinant in (2.8). Indeed, the determinant in the last line is
just the special case X
i
= i, A
j
= −a − j, B
j
= b − j + 1. Thus, by (2.8), we have a
result immediately. A particularly attractive way to write it is displayed in (2.17).
Applications of Lemma 3 are abundant, see Theorem 26 and the remarks accompa-
nying it.
In [87, Lemma 7], a determinant evaluation is given which is closely related to
Lemma 3. It was used there to establish enumeration results about shifted plane par-
titions of trapezoidal shape. It is the ﬁrst result in the lemma below. It is “tailored”
for the use in the context of q-enumeration. For plain enumeration, one would use the
second result. This is a limit case of the ﬁrst (replace X
i
by q
X
i
, A
j
by −q
−A
j
and C
by q
C
in (2.9), divide both sides by (1 −q)
n(n−1)
, and then let q → 1).
Lemma 4. Let X
1
, X
2
, . . . , X
n
, A
2
, . . . , A
n
be indeterminates. Then there hold
det
1≤i,j≤n

m
¸
i=2
n
¸
j=m
(b
i
−A
j
)(b
i
+A
j
+C)
¸
m+1≤i≤j≤n
(b
i
−a
j
)(b
i
+a
j
+C). (2.15)
If you are looking for more determinant evaluations of such a general type, then you
may want to look at [156, Lemmas A.1 and A.11] and [158, Lemma A.1].
2.3. The condensation method. This is Doron Zeilberger’s favourite method. It
allows (sometimes) to establish an elegant, eﬀortless inductive proof of a determinant
evaluation, in which the only task is to guess the result correctly.
The method is often attributed to Charles Ludwig Dodgson [38], better known as
Lewis Carroll. However, the identity on which it is based seems to be actually due to
P. Desnanot (see [119, vol. I, pp. 140–142]; with the ﬁrst rigorous proof being probably
due to Jacobi, see [18, Ch. 4] and [79, Sec. 3]). This identity is the following.
Proposition 10. Let A be an n n matrix. Denote the submatrix of A in which rows
i
1
, i
2
, . . . , i
k
and columns j
1
, j
2
, . . . , j
k
are omitted by A
j
1
,j
2
,...,j
k
i
1
,i
2
,...,i
k
. Then there holds
det A det A
1,n
1,n
= det A
1
1
det A
n
n
−det A
n
1
det A
1
n
. (2.16)
So, what is the point of this identity? Suppose you are given a family (det M
n
)
n≥0
of determinants, M
n
being an n n matrix, n = 0, 1, . . . . Maybe M
n
= M
n
(a, b)
is the matrix underlying the determinant in (1.2). Suppose further that you have
already worked out a conjecture for the evaluation of det M
n
(a, b) (we did in fact already
evaluate this determinant in Section 2.2, but let us ignore that for the moment),
det M
n
(a, b) := det
1≤i,j≤n

1,n
1,n
= M
n−2
(a, b). (2.18)
12 C. KRATTENTHALER
For, because of (2.18), Desnanot’s identity (2.16), with A = M
n
(a, b), gives a recur-
rence which expresses det M
n
(a, b) in terms of quantities of the form det M
n−1
( . ) and
det M
n−2
( . ). So, it just remains to check the conjecture (2.17) for n = 0 and n = 1, and
that the right-hand side of (2.17) satisﬁes the same recurrence, because that completes
a perfect induction with respect to n. (What we have described here is basically the
contents of [197]. For a bijective proof of Proposition 10 see [200].)
Amdeberhan (private communication) discovered that in fact the determinant evalu-
ation (2.8) itself (which we used to evaluate the determinant (1.2) for the ﬁrst time) can
be proved by condensation. The reader will easily ﬁgure out the details. Furthermore,
the condensation method also proves the determinant evaluations (3.35) and (3.36).
(Also this observation is due to Amdeberhan [2].) At another place, condensation was
used by Eisenk¨olbl [41] in order to establish a conjecture by Propp [138, Problem 3]
about the enumeration of rhombus tilings of a hexagon where some triangles along the
border of the hexagon are missing.
The reader should observe that crucial for a successful application of the method is
the existence of (at least) two parameters (in our example these are a and b), which
help to still stay within the same family of matrices when we take minors of our original
matrix (compare (2.18)). (See the last paragraph of Section 2.1 for a few hints of how
to introduce more parameters into your determinant, in the case that you are short of
parameters.) Obviously, aside from the fact that we need at least two parameters, we
can hope for a success of condensation only if our determinant is of a special kind.
2.4. The “identification of factors” method. This is the method that I ﬁnd
most convenient to work with, once you encounter a determinant that is not amenable
to an evaluation using the previous recipes. It is best to explain this method along with
an example. So, let us consider the determinant in (1.3). Here it is, together with its,
at this point, unproven evaluation,
det
0≤i,j≤n−1

µ +i +j
2i −j

= (−1)
χ(n≡3 mod 4)
2
(
n−1
2
)
n−1
¸
i=1
(µ +i + 1)
(i+1)/2

−µ −3n +i +
3
2

i/2
(i)
i
, (2.19)
where χ(/) = 1 if / is true and χ(/) = 0 otherwise, and where the shifted factorial
(a)
k
is deﬁned by (a)
k
:= a(a + 1) (a +k −1), k ≥ 1, and (a)
0
:= 1.
As was already said in the Introduction, this determinant belongs to a diﬀerent
category of diﬃculty of evaluation, so that nothing what was presented so far will
immediately work on that determinant.
Nevertheless, I claim that the procedure which we chose to evaluate the Vandermonde
determinant works also with the above determinant. To wit:
1. Identiﬁcation of factors
2. Determination of degree bound
3. Computation of the multiplicative constant.
You will say: ‘A moment please! The reason that this procedure worked so smoothly
for the Vandermonde determinant is that there are so many (to be precise: n) variables
at our disposal. On the contrary, the determinant in (2.19) has exactly one (!) variable.’
ADVANCED DETERMINANT CALCULUS 13
Yet — and this is the point that I want to make here — it works, in spite of having
just one variable at our disposal !.
What we want to prove in the ﬁrst step is that the right-hand side of (2.19) divides the
determinant. For example, we would like to prove that (µ+n) divides the determinant
(actually, (µ + n)
(n+1)/3
, we will come to that in a moment). Equivalently, if we set
µ = −n in the determinant, then it should vanish. How could we prove that? Well, if
it vanishes then there must be a linear combination of the columns, or of the rows, that
vanishes. So, let us ﬁnd such a linear combination of columns or rows. Equivalently, for
µ = −n we ﬁnd a vector in the kernel of the matrix in (2.19), respectively its transpose.
More generally (and this addresses that we actually want to prove that (µ +n)
(n+1)/3
divides the determinant):
For proving that (µ + n)
E
divides the determinant, we ﬁnd E linear inde-
pendent vectors in the kernel.
(For a formal justiﬁcation that this does indeed suﬃce, see Section 2 of [91], and in
particular the Lemma in that section.)
Okay, how is this done in practice? You go to your computer, crank out these vectors
in the kernel, for n = 1, 2, 3, . . . , and try to make a guess what they are in general.
To see how this works, let us do it in our example. What the computer gives is the
following (we are using Mathematica here):
In[1]:= V[2]
Out[1]= ¦0, c[1]¦
In[2]:= V[3]
Out[2]= ¦0, c[2], c[2]¦
In[3]:= V[4]
Out[3]= ¦0, c[1], 2 c[1], c[1]¦
In[4]:= V[5]
Out[4]= ¦0, c[1], 3 c[1], c[3], c[1]¦
In[5]:= V[6]
Out[5]= ¦0, c[1], 4 c[1], 2 c[1] + c[4], c[4], c[1]¦
In[6]:= V[7]
Out[6]= ¦0, c[1], 5 c[1], c[3], -10 c[1] + 2 c[3], -5 c[1] + c[3], c[1]¦
In[7]:= V[8]
Out[7]= ¦0, c[1], 6 c[1], c[3], -25 c[1] + 3 c[3], c[5], -9 c[1] + c[3], c[1]¦
In[8]:= V[9]
Out[8]= ¦0, c[1], 7 c[1], c[3], -49 c[1] + 4 c[3],
` -28 c[1] + 2 c[3] + c[6], c[6], -14 c[1] + c[3], c[1]¦
In[9]:= V[10]
14 C. KRATTENTHALER
Out[9]= ¦0, c[1], 8 c[1], c[3], -84 c[1] + 5 c[3], c[5],
` 196 c[1] - 10 c[3] + 2 c[5], 98 c[1] - 5 c[3] + c[5], -20 c[1] + c[3],
` c[1]¦
In[10]:= V[11]
Out[10]= ¦0, c[1], 9 c[1], c[3], -132 c[1] + 6 c[3], c[5],
` 648 c[1] - 25 c[3] + 3 c[5], c[7], 234 c[1] - 9 c[3] + c[5],
` -27 c[1] + c[3], c[1]¦
Here, V [n] is the generic vector (depending on the indeterminates c[i]) in the kernel of
the matrix in (2.19) with µ = −n. For convenience, let us denote this matrix by M
n
.
You do not have to stare at these data for long to see that, in particular,
the vector (0, 1) is in the kernel of M
2
,
the vector (0, 1, 1) is in the kernel of M
3
,
the vector (0, 1, 2, 1) is in the kernel of M
4
,
the vector (0, 1, 3, 3, 1) is in the kernel of M
5
(set c[1] = 1 and c[3] = 3),
the vector (0, 1, 4, 6, 4, 1) is in the kernel of M
6
(set c[1] = 1 and c[4] = 4), etc.
Apparently,

0,

n−2
0

,

n−2
1

,

n−2
2

, . . . ,

n−2
n−2

(2.20)
is in the kernel of M
n
. That was easy! But we need more linear combinations. Take
a closer look, and you will see that the pattern persists (set c[1] = 0 everywhere, etc.).
It will take you no time to work out a full-ﬂedged conjecture for (n + 1)/3| linear
independent vectors in the kernel of M
n
.
Of course, there remains something to be proved. We need to actually prove that our
guessed vectors are indeed in the kernel. E.g., in order to prove that the vector (2.20)
is in the kernel, we need to verify that
n−1
¸
j=1

n −2
j −1

−n +i +j
2i −j

= 0
for i = 0, 1, . . . , n −1. However, verifying binomial identities is pure routine today, by
means of Zeilberger’s algorithm [194, 196] (see Footnote 5 in the Introduction).
Next you perform the same game with the other factors of the right-hand side product
of (2.19). This is not much more diﬃcult. (See Section 3 of [91] for details. There,
slightly diﬀerent vectors are used.)
Thus, we would have ﬁnished the ﬁrst step, “identiﬁcation of factors,” of our plan: We
have proved that the right-hand side of (2.19) divides the determinant as a polynomial
in µ.
The second step, “determination of degree bound,” consists of determining the (max-
imal) degree in µ of determinant and conjectured result. As is easily seen, this is

n
2

in each case.
The arguments thus far show that the determinant in (2.19) must equal the right-
hand side times, possibly, some constant. To determine this constant in the third
step, “computation of the multiplicative constant,” one compares coeﬃcients of x
(
n
2
)
on
ADVANCED DETERMINANT CALCULUS 15
both sides of (2.19). This is an enjoyable exercise. (Consult [91] if you do not want
to do it yourself.) Further successful applications of this procedure can be found in
[27, 30, 42, 89, 90, 92, 94, 97, 132].
Having done that, let me point out that most of the individual steps in this sort of
calculation can be done (almost) automatically. In detail, what did we do? We had to
1. Guess the result. (Indeed, without the result we could not have got started.)
2. Guess the vectors in the kernel.
3. Establish a binomial (hypergeometric) identity.
4. Determine a degree bound.
5. Compute a particular value or coeﬃcient in order to determine the multiplicative
constant.
As I explain in Appendix A, guessing can be largely automatized. It was already
mentioned in the Introduction that proving binomial (hypergeometric) identities can
be done by the computer, thanks to the “WZ-machinery” [130, 190, 194, 195, 196] (see
Footnote 5). Computing the degree bound is (in most cases) so easy that no computer is
needed. (You may use it if you want.) It is only the determination of the multiplicative
constant (item 5 above) by means of a special evaluation of the determinant or the
evaluation of a special coeﬃcient (in our example we determined the coeﬃcient of µ
(
n
2
)
)
for which I am not able to oﬀer a recipe so that things could be carried out on a
computer.
The reader should notice that crucial for a successful application of the method
is the existence of (at least) one parameter (in our example this is µ) to be able to
apply the polynomiality arguments that are the “engine” of the method. If there is no
parameter (such as in the determinant in Conjecture 49, or in the determinant (3.46)
which would solve the problem of q-enumerating totally symmetric plane partitions),
then we even cannot get started. (See the last paragraph of Section 2.1 for a few hints
of how to introduce a parameter into your determinant, in the case that you are short
of a parameter.)
On the other hand, a signiﬁcant advantage of the “identiﬁcation of factors method”
is that not only is it capable of proving evaluations of the form
det(M) = CLOSED FORM,
(where CLOSED FORM means a product/quotient of “nice” factors, such as (2.19) or
(2.17)), but also of proving evaluations of the form
det(M) = (CLOSED FORM) (UGLY POLYNOMIAL), (2.21)
where, of course, M is a matrix containing (at least) one parameter, µ say. Exam-
ples of such determinant evaluations are (3.38), (3.39), (3.45) or (3.48). (The UGLY
POLYNOMIAL in (3.38), (3.39) and (3.48) is the respective sum on the right-hand
side, which in neither case can be simpliﬁed).
How would one approach the proof of such an evaluation? For one part, we already
know. “Identiﬁcation of factors” enables us to show that (CLOSED FORM) divides
det(M) as a polynomial in µ. Then, comparison of degrees in µ on both sides of
(2.21) yields that (UGLY POLYNOMIAL) is a (at this point unknown) polynomial in
16 C. KRATTENTHALER
µ of some maximal degree, m say. How can we determine this polynomial? Nothing
“simpler” than that: We ﬁnd m+ 1 values e such that we are able to evaluate det(M)
at µ = e. If we then set µ = e in (2.21) and solve for (UGLY POLYNOMIAL), then we
obtain evaluations of (UGLY POLYNOMIAL) at m + 1 diﬀerent values of µ. Clearly,
this suﬃces to ﬁnd (UGLY POLYNOMIAL), e.g., by Lagrange interpolation.
I put “simpler” in quotes, because it is here where the crux is: We may not be able
to ﬁnd enough such special evaluations of det(M). In fact, you may object: ‘Why all
these complications? If we should be able to ﬁnd m + 1 special values of µ for which
we are able to evaluate det(M), then what prevents us from evaluating det(M) as a
whole, for generic µ?’ When I am talking of evaluating det(M) for µ = e, then what I
have in mind is that the evaluation of det(M) at µ = e is “nice” (i.e., gives a “closed
form,” with no “ugly” expression involved, such as in (2.21)), which is easier to identify
(that is, to guess; see Appendix A) and in most cases easier to prove. By experience,
such evaluations are rare. Therefore, the above described procedure will only work if
the degree of (UGLY POLYNOMIAL) is not too large. (If you are just a bit short of
evaluations, then ﬁnding other informations about (UGLY POLYNOMIAL), like the
leading coeﬃcient, may help to overcome the problem.)
To demonstrate this procedure by going through a concrete example is beyond the
scope of this article. We refer the reader to [28, 43, 50, 51, 89, 90] for places where this
procedure was successfully used to solve diﬃcult enumeration problems on rhombus
tilings, respectively prove a conjectured constant term identity.
2.5. A differential/difference equation method. In this section I outline a
method for the evaluation of determinants, often used by Vitaly Tarasov and Alexander
Varchenko, which, as the preceding method, also requires (at least) one parameter.
Suppose we are given a matrix M = M(z), depending on the parameter z, of which
we want to compute the determinant. Furthermore, suppose we know that M satisﬁes
a diﬀerential equation of the form
d
dz
M(z) = T(z)M(z), (2.22)
where T(z) is some other known matrix. Then, by elementary linear algebra, we obtain
a diﬀerential equation for the determinant,
d
dz
det M(z) = Tr(T(z)) det M(z), (2.23)
which is usually easy to solve. (In fact, the diﬀerential operator in (2.22) and (2.23)
could be replaced by any operator. In particular, we could replace d/dz by the diﬀerence
operator with respect to z, in which case (2.23) is usually easy to solve as well.)
Any method is best illustrated by an example. Let us try this method on the deter-
minant (1.2). Right, we did already evaluate this determinant twice (see Sections 2.2
and 2.3), but let us pretend that we have forgotten all this.
Of course, application of the method to (1.2) itself does not seem to be extremely
promising, because that would involve the diﬀerentiation of binomial coeﬃcients. So,
ADVANCED DETERMINANT CALCULUS 17
let us ﬁrst take some factors out of the determinant (as we also did in Section 2.2),
det
1≤i,j≤n

.
Let us denote the matrix underlying the determinant on the right-hand side of this
equation by M
n
(a). In order to apply the above method, we have need for a matrix
T
n
(a) such that
d
da
M
n
(a) = T
n
(a)M
n
(a). (2.24)
Similar to the procedure of Section 2.6, the best idea is to go to the computer, crank
out T
n
(a) for n = 1, 2, 3, 4, . . . , and, out of the data, make a guess for T
n
(a). Indeed, it
suﬃces that I display T
5
(a),

(in this display, the ﬁrst line contains columns 1, 2, 3 of T
5
(a), while the second line
contains the remaining columns), so that you are forced to conclude that, apparently,
it must be true that
T
n
(a) =

n −i
j −i

n−i−1
¸
k=0

j −i −1
k

(−1)
k
a +b +n −i −k

1≤i,j,≤n
.
That (2.24) holds with this choice of T
n
(a) is then easy to verify. Consequently, by
means of (2.23), we have
d
da
det M
n
(a) =

n−1
¸
=1
n −
a +b +

det M
n
(a),
so that
M
n
(a) = constant
n−1
¸
=1
(a +b +)
n−
. (2.25)
The constant is found to be (−1)
(
n
2
)
¸
n−1
=0
!, e.g., by dividing both sides of (2.25) by
a
(
n
2
)
, letting a tend to inﬁnity, and applying (2.2) to the remaining determinant.
18 C. KRATTENTHALER
More sophisticated applications of this method (actually, of a version for systems of
diﬀerence operators) can be found in [175, Proof of Theorem 5.14] and [176, Proofs of
Theorems 5.9, 5.10, 5.11], in the context of the Knizhnik–Zamolodchikov equations.
2.6. LU-factorization. This is George Andrews’ favourite method. Starting point
is the well-known fact (see [53, p. 33ﬀ]) that, given a square matrix M, there exists,
under suitable, not very stringent conditions (in particular, these are satisﬁed if all
top-left principal minors of M are nonzero), a unique lower triangular matrix L and a
unique upper diagonal matrix U, the latter with all entries along the diagonal equal to
1, such that
M = L U. (2.26)
This unique factorization of the matrix M is known as the L(ower triangular)U(pper
triangular)-factorization of M, or as well as the Gauß decomposition of M.
Equivalently, for a square matrix M (satisfying these conditions) there exists a unique
lower triangular matrix L and a unique upper triangular matrix U, the latter with all
entries along the diagonal equal to 1, such that
M U = L. (2.27)
Clearly, once you know L and U, the determinant of M is easily computed, as it equals
the product of the diagonal entries of L.
Now, let us suppose that we are given a family (M
n
)
n≥0
of matrices, where M
n
is
an n n matrix, n = 0, 1, . . . , of which we want to compute the determinant. Maybe
M
n
is the determinant in (1.3). By the above, we know that (normally) there exist
uniquely determined matrices L
n
and U
n
, n = 0, 1, . . . , L
n
being lower triangular, U
n
being upper triangular with all diagonal entries equal to 1, such that
M
n
U
n
= L
n
. (2.28)
However, we do not know what the matrices L
n
and U
n
are. What George Andrews
does is that he goes to his computer, cranks out L
n
and U
n
for n = 1, 2, 3, 4, . . . (this
just amounts to solving a system of linear equations), and, out of the data, tries to
guess what the coeﬃcients of the matrices L
n
and U
n
are. Once he has worked out a
guess, he somehow proves that his guessed matrices L
n
and U
n
do indeed satisfy (2.28).
This program is carried out in [10] for the family of determinants in (1.3). As it turns
out, guessing is really easy, while the underlying hypergeometric identities which are
needed for the proof of (2.28) are (from a hypergeometric viewpoint) quite interesting.
For a demonstration of the method of LU-factorization, we will content ourselves
here with trying the method on the Vandermonde determinant. That is, let M
n
be the
determinant in (2.1). We go to the computer and crank out the matrices L
n
and U
n
for small values of n. For the purpose of guessing, it suﬃces that I just display the
matrices L
5
and U
5
. They are
ADVANCED DETERMINANT CALCULUS 19
L5 =






1 0 0
1 (X2 − X1) 0
1 (X3 − X1) (X3 −X1)(X3 −X2)
1 (X4 − X1) (X4 −X1)(X4 −X2)
1 (X5 − X1) (X5 −X1)(X5 −X2)
0 0
0 0
0 0
(X4 −X1)(X4 −X2)(X4 − X3) 0
(X5 −X1)(X5 −X2)(X5 − X3) (X5 −X1)(X5 −X2)(X5 − X3)(X5 − X4)






(in this display, the ﬁrst line contains columns 1, 2, 3 of L
5
, while the second line contains
the remaining columns), and
U5 =






1 −e1(X1) e2(X1, X2) −e3(X1, X2, X3) e4(X1, X2, X3, X4)
0 1 −e1(X1, X2) e2(X1, X2, X3) −e3(X1, X2, X3, X4)
0 0 1 −e1(X1, X2, X3) e2(X1, X2, X3, X4)
0 0 0 1 −e1(X1, X2, X3, X4)
0 0 0 0 1






,
where e
m
(X
1
, X
2
, . . . , X
s
) =
¸
1≤i
1
<···<im≤s
X
i
1
X
im
denotes the m-th elementary
symmetric function.
Having seen that, it will not take you for long to guess that, apparently, L
n
is given
by
L
n
=

j−1
¸
k=1
(X
i
−X
k
)

1≤i,j≤n
,
and that U
n
is given by
U
n
=

(−1)
j−i
e
j−i
(X
1
, . . . , X
j−1
)

1≤i,j≤n
,
where, of course, e
m
(X
1
, . . . ) := 0 if m < 0. That (2.28) holds with these choices of L
n
and U
n
is easy to verify. Thus, the Vandermonde determinant equals the product of
diagonal entries of L
n
, which is exactly the product on the right-hand side of (2.1).
Applications of LU-factorization are abundant in the work of George Andrews [4,
5, 6, 7, 8, 10]. All of them concern solutions to diﬃcult enumeration problems on
various types of plane partitions. To mention another example, Aomoto and Kato [11,
Theorem 3] computed the LU-factorization of a matrix which arose in the theory of
q-diﬀerence equations, thus proving a conjecture by Mimachi [118].
Needless to say that this allows for variations. You may try to guess (2.26) directly
(and not its variation (2.27)), or you may try to guess the U(pper triangular)L(ower
triangular) factorization, or its variation in the style of (2.27). I am saying this because
it may be easy to guess the form of one of these variations, while it can be very diﬃcult
to guess the form of another.
It should be observed that the way LU-factorization is used here in order to evaluate
determinants is very much in the same spirit as “identiﬁcation of factors” as described in
the previous section. In both cases, the essential steps are to ﬁrst guess something, and
then prove the guess. Therefore, the remarks from the previous section about guessing
20 C. KRATTENTHALER
and proving binomial (hypergeometric) identities apply here as well. In particular, for
guessing you are once more referred to Appendix A.
It is important to note that, as opposed to “condensation” or “identiﬁcation of fac-
tors,” LU-factorization does not require any parameter. So, in principle, it is applicable
to any determinant (which satisﬁes the aforementioned conditions). If there are limita-
tions, then, from my experience, it is that the coeﬃcients which have to be guessed in
LU-factorization tend to be more complicated than in “identiﬁcation of factors”. That
is, guessing (2.28) (or one of its variations) may sometimes be not so easy.
2.7. Hankel determinants. A Hankel determinant is a determinant of a matrix
which has constant entries along antidiagonals, i.e., it is a determinant of the form
det
1≤i,j,≤n
(c
i+j
).
If you encounter a Hankel determinant, which you think evaluates nicely, then expect
the evaluation of your Hankel determinant to be found within the domain of continued
fractions and orthogonal polynomials. In this section I explain what this connection is.
To make things concrete, let us suppose that we want to evaluate
det
0≤i,j≤n−1
(B
i+j+2
), (2.29)
where B
k
denotes the k-th Bernoulli number. (The Bernoulli numbers are deﬁned via
their generating function,
¸
∞
k=0
B
k
z
k
/k! = z/(e
z
− 1).) You have to try hard if you
want to ﬁnd an evaluation of (2.29) explicitly in the literature. Indeed, you can ﬁnd
it, hidden in Appendix A.5 of [108]. However, even if you are not able to discover this
reference (which I would not have as well, unless the author of [108] would not have
drawn my attention to it), there is a rather straight-forward way to ﬁnd an evaluation
of (2.29), which I outline below. It is based on the fact, and this is the main point of
this section, that evaluations of Hankel determinants like (2.29) are, at least implicitly,
in the literature on the theory of orthogonal polynomials and continued fractions, which
is very accessible today.
So, let us review the relevant facts about orthogonal polynomials and continued
fractions (see [76, 81, 128, 174, 186, 188] for more information on these topics).
We begin by citing the result, due to Heilermann, which makes the connection be-
tween Hankel determinants and continued fractions.
Theorem 11. (Cf. [188, Theorem 51.1] or [186, Corollaire 6, (19), on p. IV-17]). Let
(µ
k
)
k≥0
be a sequence of numbers with generating function
¸
∞
k=0
µ
k
x
k
written in the
form
∞
¸
k=0
µ
k
x
k
=
µ
0
1 +a
0
x −
b
1
x
2
1 +a
1
x −
b
2
x
2
1 +a
2
x −
. (2.30)
Then the Hankel determinant det
0≤i,j≤n−1
(µ
i+j
) equals µ
n
0
b
n−1
1
b
n−2
2
b
2
n−2
b
n−1
.
(We remark that a continued fraction of the type as in (2.30) is called a J-fraction.)
Okay, that means we would have evaluated (2.29) once we are able to explicitly
expand the generating function
¸
∞
k=0
B
k+2
x
k
in terms of a continued fraction of the
ADVANCED DETERMINANT CALCULUS 21
form of the right-hand side of (2.30). Using the tools explained in Appendix A, it is
easy to work out a conjecture,
∞
¸
k=0
B
k+2
x
k
=
1/6
1 −
b
1
x
2
1 −
b
2
x
2
1 −
, (2.31)
where b
i
= −i(i + 1)
2
(i + 2)/4(2i + 1)(2i + 3), i = 1, 2, . . . . If we would ﬁnd this
expansion in the literature then we would be done. But if not (which is the case here),
how to prove such an expansion? The key is orthogonal polynomials.
A sequence (p
n
(x))
n≥0
of polynomials is called (formally) orthogonal if p
n
(x) has de-
gree n, n = 0, 1, . . . , and if there exists a linear functional L such that L(p
n
(x)p
m
(x)) =
δ
mn
c
n
for some sequence (c
n
)
n≥0
of nonzero numbers, with δ
m,n
denoting the Kronecker
delta (i.e., δ
m,n
= 1 if m = n and δ
m,n
= 0 otherwise).
The ﬁrst important theorem in the theory of orthogonal polynomials is Favard’s
Theorem, which gives an unexpected characterization for sequences of orthogonal poly-
nomials, in that it completely avoids the mention of the functional L.
Theorem 12. (Cf. [186, Th´eor`eme 9 on p. I-4] or [188, Theorem 50.1]). Let (p
n
(x))
n≥0
be a sequence of monic polynomials, the polynomial p
n
(x) having degree n, n = 0, 1, . . . .
Then the sequence (p
n
(x)) is (formally) orthogonal if and only if there exist sequences
(a
n
)
n≥1
and (b
n
)
n≥1
, with b
n
= 0 for all n ≥ 1, such that the three-term recurrence
p
n+1
(x) = (a
n
+x)p
n
(x) −b
n
p
n−1
(x), for n ≥ 1, (2.32)
holds, with initial conditions p
0
(x) = 1 and p
1
(x) = x +a
0
.
What is the connection between orthogonal polynomials and continued fractions?
This question is answered by the next theorem, the link being the generating function
of the moments.
Theorem 13. (Cf. [188, Theorem 51.1] or [186, Proposition 1, (7), on p. V-5]). Let
(p
n
(x))
n≥0
be a sequence of monic polynomials, the polynomial p
n
(x) having degree n,
which is orthogonal with respect to some functional L. Let
p
n+1
(x) = (a
n
+x)p
n
(x) −b
n
p
n−1
(x) (2.33)
be the corresponding three-term recurrence which is guaranteed by Favard’s theorem.
Then the generating function
¸
∞
k=0
µ
k
x
k
for the moments µ
k
= L(x
k
) satisﬁes (2.30)
with the a
i
’s and b
i
’s being the coeﬃcients in the three-term recurrence (2.33).
Thus, what we have to do is to ﬁnd orthogonal polynomials (p
n
(x))
n≥0
, the three-term
recurrence of which is explicitly known, and which are orthogonal with respect to some
linear functional L whose moments L(x
k
) are exactly equal to B
k+2
. So, what would
be very helpful at this point is some sort of table of orthogonal polynomials. Indeed,
there is such a table for hypergeometric and basic hypergeometric orthogonal polynomi-
als, proposed by Richard Askey (therefore called the “Askey table”), and compiled by
Koekoek and Swarttouw [81].
Indeed, in Section 1.4 of [81], we ﬁnd the family of orthogonal polynomials that is
of relevance here, the continuous Hahn polynomials, ﬁrst studied by Atakishiyev and
Suslov [13] and Askey [12]. These polynomials depend on four parameters, a, b, c, d. It
22 C. KRATTENTHALER
is just the special choice a = b = c = d = 1 which is of interest to us. The theorem
below lists the relevant facts about these special polynomials.
Theorem 14. The continuous Hahn polynomials with parameters a = b = c = d = 1,
(p
n
(x))
n≥0
, are the monic polynomials deﬁned by
p
n
(x) = (
√
−1)
n (n + 1)!
2
(n + 2)!
(2n + 2)!
∞
¸
k=0
(−n)
k
(n + 3)
k
(1 +x
√
−1)
k
k! (k + 1)!
2
, (2.34)
with the shifted factorial (a)
k
deﬁned as previously (see (2.19)). These polynomials
satisfy the three-term recurrence
p
n+1
(x) = xp
n
(x) +
n(n + 1)
2
(n + 2)
4(2n + 1)(2n + 3)
p
n−1
(x). (2.35)
They are orthogonal with respect to the functional L which is given by
L(p(x)) =
π
2

. (2.39)
ADVANCED DETERMINANT CALCULUS 23
The idea of using continued fractions and/or orthogonal polynomials for the evalua-
tion of Hankel determinants has been also exploited in [1, 35, 113, 114, 115, 116]. Some
of these results are exhibited in Theorem 52. See the remarks after Theorem 52 for
pointers to further Hankel determinant evaluations.
2.8. Miscellaneous. This section is a collection of various further results on deter-
minant evaluation of the general sort, which I personally like, regardless whether they
may be more or less useful.
Let me begin with a result by Strehl and Wilf [173, Sec. II], a special case of which was
already in the seventies advertised by van der Poorten [131, Sec. 4] as ‘a determinant
evaluation that should be better known’. (For a generalization see [78].)
Lemma 16. Let f(x) be a formal power series. Then for any positive integer n there
holds
det
1≤i,j≤n

d
dx

i−1
f(x)
a
j

=

f

(x)
f(x)

(
n
2
)
f(x)
a
1
+···+an
¸
1≤i<j≤n
(a
j
−a
i
). (2.40)
By specializing, this result allows for the quick proof of various, sometimes surprising,
determinant evaluations, see Theorems 53 and 54.
An extremely beautiful determinant evaluation is the evaluation of the determinant
of the circulant matrix.
Theorem 17. Let n by a ﬁxed positive integer, and let a
0
, a
1
, . . . , a
n−1
be indetermi-
nates. Then
det

=
n−1
¸
i=0
(a
0
+ω
i
a
1
+ω
2i
a
2
+ +ω
(n−1)i
a
n−1
),
(2.41)
where ω is a primitive n-th root of unity.
Actually, the circulant determinant is just a very special case in a whole family of
determinants, called group determinants. This would bring us into the vast territory of
group representation theory, and is therefore beyond the scope of this article. It must
suﬃce to mention that the group determinants were in fact the cause of birth of group
representation theory (see [99] for a beautiful introduction into these matters).
The next theorem does actually not give the evaluation of a determinant, but of a
Pfaﬃan. The Pfaﬃan Pf(A) of a skew-symmetric (2n) (2n) matrix A is deﬁned by
Pf(A) =
¸
π
(−1)
c(π)
¸
(ij)∈π
A
ij
,
where the sum is over all perfect matchings π of the complete graph on 2n vertices,
where c(π) is the crossing number of π, and where the product is over all edges (ij),
i < j, in the matching π (see e.g. [169, Sec. 2]). What links Pfaﬃans so closely to
24 C. KRATTENTHALER
determinants is (aside from similarity of deﬁnitions) the fact that the Pfaﬃan of a
skew-symmetric matrix is, up to sign, the square root of its determinant. That is,
det(A) = Pf(A)
2
for any skew-symmetric (2n) (2n) matrix A (cf. [169, Prop. 2.2]).
9
Pfaﬃans play an important role, for example, in the enumeration of plane partitions,
due to the results by Laksov, Thorup and Lascoux [98, Appendix, Lemma (A.11)] and
Okada [123, Theorems 3 and 4] on sums of minors of a given matrix (a combinatorial
view as enumerating nonintersecting lattice paths with varying starting and/or ending
points has been given by Stembridge [169, Theorems 3.1, 3.2, and 4.1]), and their
generalization in form of the powerful minor summation formulas due to Ishikawa and
Wakayama [69, Theorems 2 and 3].
Exactly in this context, the context of enumeration of plane partitions, Gordon [58,
implicitly in Sec. 4, 5] (see also [169, proof of Theorem 7.1]) proved two extremely useful
reductions of Pfaﬃans to determinants.
Lemma 18. Let (g
i
) be a sequence with the property g
−i
= g
i
, and let N be a positive
integer. Then
Pf
1≤i<j≤2N

= X det
1≤i,j≤N
(g
i−j
−g
i+j
). (2.43)
(In these statements only one half of the entries of the Pfaﬃan is given, the other half
being uniquely determined by skew-symmetry).
This result looks somehow technical, but its usefulness was suﬃciently proved by
its applications in the enumeration of plane partitions and tableaux in [58] and [169,
Sec. 7].
Another technical, but useful result is due to Goulden and Jackson [61, Theorem 2.1].
Lemma 19. Let F
m
(t), G
m
(t) and H
m
(t) by formal power series, with H
m
(0) = 0,
m = 0, 1, . . . , n −1. Then for any positive integer n there holds
det
0≤i,j,≤n−1

CT

F
j
(t)
H
j
(t)
i
G
i
(H
j
(t))

= det
0≤i,j≤n−1

CT

F
j
(t)
H
j
(t)
i
G
i
(0)

, (2.44)
where CT(f(t)) stands for the constant term of the Laurent series f(t).
What is the value of this theorem? In some cases, out of a given determinant eval-
uation, it immediately implies a more general one, containing (at least) one more pa-
rameter. For example, consider the determinant evaluation (3.30). Choose F
j
(t) =
t
j
(1 +t)
µ+j
, H
j
(t) = t
2
/(1 +t), and G
i
(t) such that G
i
(t
2
/(1 +t)) = (1 +t)
k
+(1 +t)
−k
for a ﬁxed k (such a choice does indeed exist; see [61, proof of Cor. 2.2]) in Lemma 19.
This yields
det
0≤i,j≤n−1

µ +k +i +j
2i −j

+

µ −k +i +j
2i −j

= det
0≤i,j≤n−1

2

µ +i +j
2i −j

.
9
Another point of view, beautifully set forth in [79], is that “Pfaﬃans are more fundamental than
determinants, in the sense that determinants are merely the bipartite special case of a general sum
over matchings.”
ADVANCED DETERMINANT CALCULUS 25
Thus, out of the validity of (3.30), this enables to establish the validity of (3.32), and
even of (3.33), by choosing F
j
(t) and H
j
(t) as above, but G
i
(t) such that G
i
(t
2
/(1+t)) =
(1 +t)
x
i
+ (1 +t)
−x
i
, i = 0, 1, . . . , n −1.
3. A list of determinant evaluations
In this section I provide a list of determinant evaluations, some of which are very
frequently met, others maybe not so often. In any case, I believe that all of them
are useful or attractive, or even both. However, this is not intended to be, and cannot
possibly be, an exhaustive list of known determinant evaluations. The selection depends
totally on my taste. This may explain that many of these determinants arose in the
enumeration of plane partitions and rhombus tilings. On the other hand, it is exactly
this ﬁeld (see [138, 148, 163, 165] for more information on these topics) which is a
particular rich source of nontrivial determinant evaluations. If you do not ﬁnd “your”
determinant here, then, at least, the many references given in this section or the general
results and methods from Section 2 may turn out to be helpful.
Throughout this section we use the standard hypergeometric and basic hypergeomet-
ric notations. To wit, for nonnegative integers k the shifted factorial (a)
k
is deﬁned (as
already before) by
(a)
k
:= a(a + 1) (a +k −1),
so that in particular (a)
0
:= 1. Similarly, for nonnegative integers k the shifted q-
factorial (a; q)
k
is given by
(a; q)
k
:= (1 −a)(1 −aq) (1 −aq
k−1
),
so that (a; q)
0
:= 1. Sometimes we make use of the notations [α]
q
:= (1 −q
α
)/(1 −q),
[n]
q
! := [n]
q
[n −1]
q
[1]
q
, [0]
q
! := 1. The q-binomial coeﬃcient is deﬁned by
¸
α
k

.
Occasionally shifted (q-)factorials will appear which contain a subscript which is a
negative integer. By convention, a shifted factorial (a)
k
, where k is a negative integer, is
interpreted as (a)
k
:= 1/(a −1)(a −2) (a +k), whereas a shifted q-factorial (a; q)
k
,
where k is a negative integer, is interpreted as (a; q)
k
:= 1/(1 − q
a−1
)(1 − q
a−2
)
(1 − q
a+k
). (A uniform way to deﬁne the shifted factorial, for positive and negative k,
is by (a)
k
:= Γ(a +k)/Γ(a), respectively by an appropriate limit in case that a or a +k
is a nonpositive integer, see [62, Sec. 5.5, p. 211f]. A uniform way to deﬁne the shifted
q-factorial is by means of (a; q)
k
:= (a; q)
∞
/(aq
k
; q)
∞
, see [55, (1.2.30)].)
We begin our list with two determinant evaluations which generalize the Vander-
monde determinant evaluation (2.1) in a nonstandard way. The determinants appearing
in these evaluations can be considered as “augmentations” of the Vandermonde deter-
minant by columns which are formed by diﬀerentiating “Vandermonde-type” columns.
(Thus, these determinants can also be considered as certain generalized Wronskians.)
Occurences of the ﬁrst determinant can be found e.g. in [45], [107, App. A.16], [108,
(7.1.3)], [154], [187]. (It is called “conﬂuent alternant” in [107, 108].) The motivation
in [45] to study these determinants came from Hermite interpolation and the analysis
of linear recursion relations. In [107, App. A.16], special cases of these determinants
26 C. KRATTENTHALER
are used in the context of random matrices. Special cases arose also in the context of
transcendental number theory (see [131, Sec. 4]).
Theorem 20. Let n be a nonnegative integer, and let A
m
(X) denote the nm matrix

.
Yet another generalization of the Vandermonde determinant evaluation is found in
[21]. Multidimensional analogues are contained in [176, Theorem A.7, Eq. (A.14),
Theorem B.8, Eq. (B.11)] and [182, Part I, p. 547].
Extensions of Cauchy’s double alternant (2.7) can also be found in the literature (see
e.g. [117, 149]). I want to mention here particularly Borchardt’s variation [17] in which
the (i, j)-entry in Cauchy’s double alternant is replaced by its square,
det
1≤i,j≤n

1
(X
i
−Y
j
)
2

=
¸
1≤i<j≤n
(X
i
−X
j
)(Y
i
−Y
j
)
¸
1≤i,j≤n
(X
i
−Y
j
)
Per
1≤i,j≤n

1
X
i
−Y
j

, (3.9)
where Per M denotes the permanent of the matrix M. Thus, there is no closed form
expression such as in (2.7). This may not look that useful. However, most remarkably,
there is a (q-)deformation of this identity which did indeed lead to a “closed form evalu-
ation,” thus solving a famous enumeration problem in an unexpected way, the problem
of enumerating alternating sign matrices.
10
This q-deformation is equivalent to Izergin’s
evaluation [74, Eq. (5)] (building on results by Korepin [82]) of the partition function of
the six-vertex model under certain boundary conditions (see also [97, Theorem 8] and
[83, Ch. VII, (10.1)/(10.2)]).
10
An alternating sign matrix is a square matrix with entries 0, 1, −1, with all row and column
sums equal to 1, and such that, on disregarding the 0s, in each row and column the 1s and (−1)s
alternate. Alternating sign matrix are currently the most fascinating, and most mysterious, objects in
enumerative combinatorics. The reader is referred to [18, 19, 111, 148, 97, 198, 199] for more detailed
material. Incidentally, the “birth” of alternating sign matrices came through — determinants, see
[150].
30 C. KRATTENTHALER
Theorem 25. For any nonnegative integer n there holds
det
1≤i,j≤n

1
(X
i
−Y
j
)(qX
i
−Y
j
)

=
¸
1≤i<j≤n
(X
i
−X
j
)(Y
i
−Y
j
)
¸
1≤i,j≤n
(X
i
−Y
j
)(qX
i
−Y
j
)

¸
A
(1 −q)
2N(A)
n
¸
i=1
X
N
i
(A)
i
Y
N
i
(A)
i
¸
i,j such that A
ij
=0
(α
i,j
X
i
−Y
j
), (3.10)
where the sum is over all n n alternating sign matrices A = (A
ij
)
1≤i,j≤n
, N(A) is
the number of (−1)s in A, N
i
(A) (respectively N
i
(A)) is the number of (−1)s in the
i-th row (respectively column) of A, and α
ij
= q if
¸
j
k=1
A
ik
=
¸
i
k=1
A
kj
, and α
ij
= 1
otherwise.
Clearly, equation (3.9) results immediately from (3.10) by setting q = 1. Roughly,
Kuperberg’s solution [97] of the enumeration of alternating sign matrices consisted of
suitably specializing the x
i
’s, y
i
’s and q in (3.10), so that each summand on the right-
hand side would reduce to the same quantity, and, thus, the sum would basically count
n n alternating sign matrices, and in evaluating the left-hand side determinant for
that special choice of the x
i
’s, y
i
’s and q. The resulting number of n n alternating
sign matrices is given in (A.1) in the Appendix. (The ﬁrst, very diﬀerent, solution
is due to Zeilberger [198].) Subsequently, Zeilberger [199] improved on Kuperberg’s
approach and succeeded in proving the reﬁned alternating sign matrix conjecture from
[111, Conj. 2]. For a diﬀerent expansion of the determinant of Izergin, in terms of Schur
functions, and a variation, see [101, Theorem q, Theorem γ].
Next we turn to typical applications of Lemma 3. They are listed in the following
theorem.
Theorem 26. Let n be a nonnegative integer, and let L
1
, L
2
, . . . , L
n
and A, B be inde-
terminates. Then there hold
det
1≤i,j≤n

=
n
¸
i=1
(A +Bi)
i−1
(n −L
i
)!
¸
1≤i<j≤n
(L
j
−L
i
). (3.14)
ADVANCED DETERMINANT CALCULUS 31
(For derivations of (3.11) and (3.12) using Lemma 3 see the proofs of Theorems 6.5
and 6.6 in [85]. For a derivation of (3.13) using Lemma 3 see the proof of Theorem 5
in [86].)
Actually, the evaluations (3.11) and (3.12) are equivalent. This is seen by observing
that
¸
L
i
+A +j
L
i
+j

q
.
Hence, replacement of A by −A−1 in (3.11) leads to (3.12) after little manipulation.
The determinant evaluations (3.11) and (3.12), and special cases thereof, are redis-
covered and reproved in the literature over and over. (This phenomenon will probably
persist.) To the best of my knowledge, the evaluation (3.11) appeared in print explicitly
for the ﬁrst time in [22], although it was (implicitly) known earlier to people in group
representation theory, as it also results from the principal specialization (i.e., set x
i
= q
i
,
i = 1, 2, . . . , N) of a Schur function of arbitrary shape, by comparing the Jacobi–Trudi
identity with the bideterminantal form (Weyl character formula) of the Schur function
(cf. [105, Ch. I, (3.4), Ex. 3 in Sec. 2, Ex. 1 in Sec. 3]; the determinants arising in the
bideterminantal form are Vandermonde determinants and therefore easily evaluated).
The main applications of (3.11)–(3.13) are in the enumeration of tableaux, plane par-
titions and rhombus tilings. For example, the hook-content formula [163, Theorem 15.3]
for tableaux of a given shape with bounded entries follows immediately from the the-
ory of nonintersecting lattice paths (cf. [57, Cor. 2] and [169, Theorem 1.2]) and the
determinant evaluation (3.11) (see [57, Theorem 14] and [85, proof of Theorem 6.5]).
MacMahon’s “box formula” [106, Sec. 429; proof in Sec. 494] for the generating function
of plane partitions which are contained inside a given box follows from nonintersecting
lattice paths and the determinant evaluation (3.12) (see [57, Theorem 15] and [85, proof
of Theorem 6.6]). The q = 1 special case of the determinant which is relevant here is
the one in (1.2) (which is the one which was evaluated as an illustration in Section 2.2).
To the best of my knowledge, the evaluation (3.13) is due to Proctor [133] who used
it for enumerating plane partitions of staircase shape (see also [86]). The determinant
evaluation (3.14) can be used to give closed form expressions in the enumeration of λ-
parking functions (an extension of the notion of k-parking functions such as in [167]), if
one starts with determinantal expressions due to Gessel (private communication). Fur-
ther applications of (3.11), in the domain of multiple (basic) hypergeometric series, are
found in [63]. Applications of these determinant evaluations in statistics are contained
in [66] and [168].
It was pointed out in [34] that plane partitions in a given box are in bijection with
rhombus tilings of a “semiregular” hexagon. Therefore, the determinant (1.2) counts
as well rhombus tilings in a hexagon with side lengths a, b, n, a, b, n. In this regard,
generalizations of the evaluation of this determinant, and of a special case of (3.13),
appear in [25] and [27]. The theme of these papers is to enumerate rhombus tilings of
a hexagon with triangular holes.
The next theorem provides a typical application of Lemma 4. For a derivation of this
determinant evaluation using this lemma see [87, proofs of Theorems 8 and 9].
32 C. KRATTENTHALER
Theorem 27. Let n be a nonnegative integer, and let L
1
, L
2
, . . . , L
n
and A be indeter-
minates. Then there holds
det
1≤i,j≤n

q
jL
i
¸
L
i
+A−j
L
i
+j

q

= q

n
i=1
iL
i
n
¸
i=1
[L
i
+A −n]
q
!
[L
i
+n]
q
! [A−2i]
q
!
¸
1≤i<j≤n

[L
i
−L
j
]
q
[L
i
+L
j
+A + 1]
q

. (3.15)
This result was used to compute generating functions for shifted plane partitions of
trapezoidal shape (see [87, Theorems 8 and 9], [134, Prop. 4.1] and [135, Theorem 1]).
Now we turn to typical applications of Lemma 5, given in Theorems 28–31 below.
All of them can be derived in just the same way as we evaluated the determinant (1.2)
in Section 2.2 (the only diﬀerence being that Lemma 5 is invoked instead of Lemma 3).
The ﬁrst application is the evaluation of a determinant whose entries are a product
of two q-binomial coeﬃcients.
Theorem 28. Let n be a nonnegative integer, and let L
1
, L
2
, . . . , L
n
and A, B be inde-
terminates. Then there holds
det
1≤i,j≤n

n−1
¸
i=0
(q
B−Y
i
−i+1
)
i
(q
Y
i
+A+B+2−2i
)
i
(q
X
i
−A−B
)
n−1
(q
X
i
+B−n+2
)
n−1
. (3.17)
As another application of Lemma 5 we list two evaluations of determinants (see below)
where the entries are, up to some powers of q, a diﬀerence of two q-binomial coeﬃcients.
A proof of the ﬁrst evaluation which uses Lemma 5 can be found in [88, proof of
Theorem 7], a proof of the second evaluation using Lemma 5 can be found in [155,
Ch. VI, '3]. Once more, the second evaluation was always (implicitly) known to people
in group representation theory, as it also results from a principal specialization (set
x
i
= q
i−1/2
, i = 1, 2, . . . ) of a symplectic character of arbitrary shape, by comparing the
symplectic dual Jacobi–Trudi identity with the bideterminantal form (Weyl character
formula) of the symplectic character (cf. [52, Cor. 24.24 and (24.18)]; the determinants
arising in the bideterminantal form are easily evaluated by means of (2.4)).
Theorem 30. Let n be a nonnegative integer, and let L
1
, L
2
, . . . , L
n
and A be indeter-
minates. Then there hold
det
1≤i,j≤n

=
n
¸
i=1
[A+ 2i −1]
q
!
[n −L
i
]
q
! [A+n +L
i
]
q
!
¸
1≤i<j≤n
[L
j
−L
i
]
q
¸
1≤i≤j≤n
[L
i
+L
j
+A]
q
. (3.19)
A special case of (3.19) was the second determinant evaluation which Andrews needed
in [4, (1.4)] in order to prove the MacMahon Conjecture (since then, ex-Conjecture)
about the q-enumeration of symmetric plane partitions. Of course, Andrews’ evaluation
proceeded by LU-factorization, while Schlosser [155, Ch. VI, '3] simpliﬁed Andrews’
proof signiﬁcantly by making use of Lemma 5. The determinant evaluation (3.18)
34 C. KRATTENTHALER
was used in [88] in the proof of reﬁnements of the MacMahon (ex-)Conjecture and the
Bender–Knuth (ex-)Conjecture. (The latter makes an assertion about the generating
function for tableaux with bounded entries and a bounded number of columns. The
ﬁrst proof is due to Gordon [59], the ﬁrst published proof [3] is due to Andrews.)
Next, in the theorem below, we list two very similar determinant evaluations. This
time, the entries of the determinants are, up to some powers of q, a sum of two q-
binomial coeﬃcients. A proof of the ﬁrst evaluation which uses Lemma 5 can be found
in [155, Ch. VI, '3]. A proof of the second evaluation can be established analogously.
Again, the second evaluation was always (implicitly) known to people in group represen-
tation theory, as it also results from a principal specialization (set x
i
= q
i
, i = 1, 2, . . . )
of an odd orthogonal character of arbitrary shape, by comparing the orthogonal dual
Jacobi–Trudi identity with the bideterminantal form (Weyl character formula) of the
orthogonal character (cf. [52, Cor. 24.35 and (24.28)]; the determinants arising in the
bideterminantal form are easily evaluated by means of (2.3)).
Theorem 31. Let n be a nonnegative integer, and let L
1
, L
2
, . . . , L
n
and A be indeter-
minates. Then there hold
det
1≤i,j≤n

¸
1≤i<j≤n
[L
j
−L
i
]
q
[L
i
+L
j
+A−2]
q
. (3.21)
A special case of (3.20) was the ﬁrst determinant evaluation which Andrews needed
in [4, (1.3)] in order to prove the MacMahon Conjecture on symmetric plane parti-
tions. Again, Andrews’ evaluation proceeded by LU-factorization, while Schlosser [155,
Ch. VI, '3] simpliﬁed Andrews’ proof signiﬁcantly by making use of Lemma 5.
Now we come to determinants which belong to a diﬀerent category what regards
diﬃculty of evaluation, as it is not possible to introduce more parameters in a substantial
way.
The ﬁrst determinant evaluation in this category that we list here is a determinant
evaluation due to Andrews [5, 6]. It solved, at the same time, Macdonald’s problem of
ADVANCED DETERMINANT CALCULUS 35
enumerating cyclically symmetric plane partitions and Andrews’ own conjecture about
the enumeration of descending plane partitions.
Theorem 32. Let µ be an indeterminate. For nonnegative integers n there holds
det
0≤i,j≤n−1

i/2
¸
(n−1)/2
i=1
(2i −1)!!
2
if n is odd.
(3.22)
The specializations of this determinant evaluation which are of relevance for the
enumeration of cyclically symmetric plane partitions and descending plane partitions
are the cases µ = 0 and µ = 1, respectively. In these cases, Macdonald, respectively
Andrews, actually had conjectures about q-enumeration. These were proved by Mills,
Robbins and Rumsey [110]. Their theorem which solves the q-enumeration of cyclically
symmetric plane partitions is the following.
Theorem 33. For nonnegative integers n there holds
det
0≤i,j≤n−1

=
¸
1≤i≤j≤n+1
1 −q
n+i+j
1 −q
2i+j−1
. (3.24)
It is somehow annoying that so far nobody was able to come up with a full q-analogue
of the Andrews determinant (3.22) (i.e., not just in the cases µ = 0 and µ = 1). This
issue is already addressed in [6, Sec. 3]. In particular, it is shown there that the result
for a natural q-enumeration of a parametric family of descending plane partitions does
not factor nicely in general, and thus does not lead to a q-analogue of (3.22). Yet, such
36 C. KRATTENTHALER
a q-analogue should exist. Probably the binomial coeﬃcient in (3.22) has to be replaced
by something more complicated than just a q-binomial times some power of q.
On the other hand, there are surprising variations of the Andrews determinant (3.22),
discovered by Douglas Zare. These can be interpreted as certain weighted enumerations
of cyclically symmetric plane partitions and of rhombus tilings of a hexagon with a
triangular hole (see [27]).
Theorem 35. Let µ be an indeterminate. For nonnegative integers n there holds
det
0≤i,j≤n−1

(n−4i−2)/2
, (3.26)
where, in abuse of notation, by α| we mean the usual ﬂoor function if α ≥ 0, however,
if α < 0 then α| must be read as 0, so that the product over i in (3.26) is indeed a
ﬁnite product.
If ω is a primitive 6th root of unity, then for nonnegative integers n there holds
det
0≤i,j≤n−1

ωδ
ij
+

2µ +i +j
j

=
(1 +ω)
n

2
3

n/2
¸
n/2
i=1
(2i −1)!!
¸
(n−1)/2
i=1
(2i −1)!!

¸
i≥0

µ + 3i +
3
2

(n−4i−1)/2

µ + 3i +
5
2

(n−4i−2)/2
(µ +n −i)
(n−4i)/2
(µ +n −i)
(n−4i−3)/2
, (3.27)
where again, in abuse of notation, by α| we mean the usual ﬂoor function if α ≥ 0,
however, if α < 0 then α| must be read as 0, so that the product over i in (3.27) is
indeed a ﬁnite product.
There are no really simple proofs of Theorems 32–35. Let me just address the issue
of proofs of the evaluation of the Andrews determinant, Theorem 32. The only direct
proof of Theorem 32 is the original proof of Andrews [5], who worked out the LU-
factorization of the determinant. Today one agrees that the “easiest” way of evaluating
the determinant (3.22) is by ﬁrst employing a magniﬁcent factorization theorem [112,
Theorem 5] due to Mills, Robbins and Rumsey, and then evaluating each of the two
resulting determinants. For these, for some reason, more elementary evaluations exist
(see in particular [10] for such a derivation). What I state below is a (straightforward)
generalization of this factorization theorem from [92, Lemma 2].
ADVANCED DETERMINANT CALCULUS 37
Theorem 36. Let Z
n
(x; µ, ν) be deﬁned by
Z
n
(x; µ, ν) := det
0≤i,j≤n−1

+
1
2
)
(i+1)/2
(2i −1)!!
. (3.31)
38 C. KRATTENTHALER
The reader should notice that the determinant in (3.30) is the third determinant from
the Introduction, (1.3). Originally, in [112, Theorem 7], Mills, Robbins and Rumsey
proved (3.30) by applying their factorization theorem (Theorem 36) the other way
round, relying on Andrews’ Theorem 32. However, in the meantime there exist short
direct proofs of (3.30), see [10, 91, 129], either by LU-factorization, or by “identiﬁcation
of factors”. A proof based on the determinant evaluation (3.35) and some combinatorial
considerations is given in [29, Remark 4.4], see the remarks after Theorem 40. As shown
in [9, 10], the determinant (3.31) can easily be transformed into a special case of the
determinant in (3.35) (whose evaluation is easily proved using condensation, see the
corresponding remarks there). Altogether, this gives an alternative, and simpler, proof
of Theorem 32.
Mills, Robbins and Rumsey needed the evaluation of (3.30) because it allowed them
to prove the (at that time) conjectured enumeration of cyclically symmetric transpose-
complementary plane partitions (see [112]). The unspecialized determinants Z
n
(x; µ, ν)
and T
n
(x; µ, ν) have combinatorial meanings as well (see [110, Sec. 4], respectively
[92, Sec. 3]), as the weighted enumeration of certain descending plane partitions and
triangularly shaped plane partitions.
It must be mentioned that the determinants Z
n
(x; µ, ν), T
n
(x; µ, ν), R
n
(x; µ, ν) do
also factor nicely for x = 2. This was proved by Andrews [7] using LU-factorization,
thus conﬁrming a conjecture by Mills, Robbins and Rumsey (see [92] for an alternative
proof by “identiﬁcation of factors”).
It was already mentioned in Section 2.8 that there is a general theorem by Goulden
and Jackson [61, Theorem 2.1] (see Lemma 19 and the remarks thereafter) which,
given the evaluation (3.30), immediately implies a generalization containing one more
parameter. (This property of the determinant (3.30) is called by Goulden and Jackson
the averaging property.) The resulting determinant evaluation had been earlier found
by Andrews and Burge [9, Theorem 1]. They derived it by showing that it can be
obtained by multiplying the matrix underlying the determinant (3.30) by a suitable
triangular matrix.
Theorem 38. Let x and y be indeterminates. For nonnegative integers n there holds
det
0≤i,j≤n−1

x +i +j
2i −j

+

y +i +j
2i −j

= (−1)
χ(n≡3 mod 4)
2
(
n
2
)
+1
n−1
¸
i=1

x+y
2
+i + 1

(i+1)/2

−
x+y
2
−3n +i +
3
2

i/2
(i)
i
, (3.32)
where χ(/) = 1 if / is true and χ(/) = 0 otherwise.
(The evaluation (3.32) does indeed reduce to (3.30) by setting x = y.)
The above described procedure of Andrews and Burge to multiply a matrix, whose
determinant is known, by an appropriate triangular matrix, and thus obtain a new
determinant evaluation, was systematically exploited by Chu [23]. He derives numerous
variations of (3.32), (3.31), and special cases of (3.13). We content ourselves with
displaying two typical identities from [23, (3.1a), (3.5a)], just enough to get an idea of
the character of these.
ADVANCED DETERMINANT CALCULUS 39
Theorem 39. Let x
0
, x
1
, . . . , x
n−1
and c be indeterminates. For nonnegative integers
n there hold
det
0≤i,j≤n−1

c +x
i
+i +j
2i −j

+

c −x
i
+i +j
2i −j

= (−1)
χ(n≡3 mod 4)
2
(
n
2
)+1
n−1
¸
i=1
(c +i + 1)
(i+1)/2

−c −3n +i +
3
2

i/2
(i)
i
(3.33)
and
det
0≤i,j≤n−1

(2i −j) + (2c + 3j + 1)(2c + 3j −1)
(c +i +j +
1
2
)(c +i +j −
1
2
)

c +i +j +
1
2
2i −j

= (−1)
χ(n≡3 mod 4)
2
(
n+1
2
)+1
n−1
¸
i=1

c +i +
1
2

(i+1)/2
(−c −3n +i + 2)
i/2
(i)
i
, (3.34)
where χ(/) = 1 if / is true and χ(/) = 0 otherwise.
The next determinant (to be precise, the special case y = 0), whose evaluation is
stated in the theorem below, seems to be closely related to the Mills–Robbins–Rumsey
determinant (3.30), although it is in fact a lot easier to evaluate. Indications that
the evaluation (3.30) is much deeper than the following evaluation are, ﬁrst, that it
does not seem to be possible to introduce a second parameter into the Mills–Robbins–
Rumsey determinant (3.30) in a similar way, and, second, the much more irregular form
of the right-hand side of (3.30) (it contains many ﬂoor functions!), as opposed to the
right-hand side of (3.35).
Theorem 40. Let x, y, n be nonnegative integers. Then there holds
det
0≤i,j≤n−1

(x +y +i +j −1)!
(x + 2i −j)! (y + 2j −i)!

=
n−1
¸
i=0
i! (x +y +i −1)! (2x +y + 2i)
i
(x + 2y + 2i)
i
(x + 2i)! (y + 2i)!
. (3.35)
This determinant evaluation is due to the author, who proved it in [90, (5.3)] as an
aside to the (much more diﬃcult) determinant evaluations which were needed there to
settle a conjecture by Robbins and Zeilberger about a generalization of the enumeration
of totally symmetric self-complementary plane partitions. (These are the determinant
evaluations of Theorems 43 and 45 below.) It was proved there by “identiﬁcation of
factors”. However, Amdeberhan [2] observed that it can be easily proved by “conden-
sation”.
Originally there was no application for (3.35). However, not much later, Ciucu [29]
found not just one application. He observed that if the determinant evaluation (3.35)
is suitably combined with his beautiful Matchings Factorization Theorem [26, Theo-
rem 1.2] (and some combinatorial considerations), then not only does one obtain simple
proofs for the evaluation of the Andrews determinant (3.22) and the Mills–Robbins–
Rumsey determinant (3.30), but also simple proofs for the enumeration of four diﬀerent
40 C. KRATTENTHALER
symmetry classes of plane partitions, cyclically symmetric plane partitions, cyclically
symmetric self-complementary plane partitions (ﬁrst proved by Kuperberg [96]), cycli-
cally symmetric transpose-complementary plane partitions (ﬁrst proved by Mills, Rob-
bins and Rumsey [112]), and totally symmetric self-complementary plane partitions (ﬁrst
proved by Andrews [8]).
A q-analogue of the previous determinant evaluation is contained in [89, Theorem 1].
Again, Amdeberhan [2] observed that it can be easily proved by means of “condensa-
tion”.
Theorem 41. Let x, y, n be nonnegative integers. Then there holds
det
0≤i,j≤n−1

=
n−1
¸
i=0
q
−2i
2 (q
2
; q
2
)
i
(q; q)
x+y+i−1
(q
2x+y+2i
; q)
i
(q
x+2y+2i
; q)
i
(q; q)
x+2i
(q; q)
y+2i
(−q
x+y+1
; q)
n−1+i
. (3.36)
The reader should observe that this is not a straightforward q-analogue of (3.35) as it
does contain the terms (−q
x+y+1
; q)
i+j
in the determinant, respectively (−q
x+y+1
; q)
n−1+i
in the denominator of the right-hand side product, which can be cleared only if q = 1.
A similar determinant evaluation, with some overlap with (3.36), was found by An-
drews and Stanton [10, Theorem 8] by making use of LU-factorization, in their “´etude”
on the Andrews and the Mills–Robbins–Rumsey determinant.
Theorem 42. Let x and E be indeterminates and n be a nonnegative integer. Then
there holds
det
0≤i,j≤n−1

q
q
yk
(q
x
; q)
k
(q
y
; q)
n−k
. (3.39)
Once more, Amdeberhan observed that, in principle, Theorem 43 as well as The-
orem 44 could be proved by means of “condensation”. However, as of now, nobody
provided a proof of the double sum identities which would establish (2.16) in these
cases.
We continue with Theorems 2 and Corollary 3 from [90].
Theorem 45. Let x, m, n be nonnegative integers with m ≤ n. Under the convention
that sums are interpreted by
B
¸
r=A+1
Expr(r) =

(x
2
+ (4n + 3)x + 2(n
2
+ 4n + 1)) n even
(2x +n + 4)(2x + 2n + 4) n odd.
(3.45)
One of the most embarrassing failures of “identiﬁcation of factors,” respectively of
LU-factorization, is the problem of q-enumeration of totally symmetric plane partitions,
as stated for example in [164, p. 289] or [165, p. 106]. It is now known for quite a
while that also this problem can be reduced to the evaluation of a certain determinant,
by means of Okada’s result [123, Theorem 4] about the sum of all minors of a given
matrix, that was already mentioned in Section 2.8. In fact, in [123, Theorem 5], Okada
succeeded to transform the resulting determinant into a reasonably simple one, so that
the problem of q-enumerating totally symmetric plane partitions reduces to resolving
the following conjecture.
Conjecture 46. For any nonnegative integer n there holds
det
1≤i,j≤n

.
While the problem of (plain) enumeration of totally symmetric plane partitions was
solved a few years ago by Stembridge [170] (by some ingenious transformations of the
determinant which results directly from Okada’s result on the sum of all minors of a
matrix), the problem of q-enumeration is still wide open. “Identiﬁcation of factors”
cannot even get started because so far nobody came up with a way of introducing a
parameter in (3.46) or any equivalent determinant (as it turns out, the parameter q
cannot serve as a parameter in the sense of Section 2.4), and, apparently, guessing the
LU-factorization is too diﬃcult.
Let us proceed by giving a few more determinants which arise in the enumeration of
rhombus tilings.
Our next determinant evaluation is the evaluation of a determinant which, on dis-
regarding the second binomial coeﬃcient, would be just a special case of (3.13), and
which, on the other hand, resembles very much the q = 1 case of (3.18). (It is the
determinant that was shown as (1.4) in the Introduction.) However, neither Lemma 3
nor Lemma 5 suﬃce to give a proof. The proof in [30] by means of “identiﬁcation of
factors” is unexpectedly diﬃcult.
Theorem 47. Let n be a positive integer, and let x and y be nonnegative integers.
Then the following determinant evaluation holds:
det
1≤i,j≤n

x +y +j
x −i + 2j

−

x +y +j
x +i + 2j

=
n
¸
j=1
(j −1)! (x +y + 2j)! (x −y + 2j + 1)
j
(x + 2y + 3j + 1)
n−j
(x +n + 2j)! (y +n −j)!
. (3.47)
This determinant evaluation is used in [30] to enumerate rhombus tilings of a certain
pentagonal subregion of a hexagon.
To see an example of diﬀerent nature, I present a determinant evaluation from [50,
Lemma 2.2], which can be considered as a determinant of a mixture of two matrices,
out of one we take all rows except the l-th, while out of the other we take just the l-th
row. The determinants of both of these matrices could be straightforwardly evaluated
by means of Lemma 3. (They are in fact equivalent to special cases of (3.13).) However,
to evaluate this mixture is much more challenging. In particular, the mixture does not
44 C. KRATTENTHALER
anymore factor completely into “nice” factors, i.e., the result is of the form (2.21), so
that for a proof one has to resort to the extension of “identiﬁcation of factors” described
there.
Theorem 48. Let n, m, l be positive integers such that 1 ≤ l ≤ n. Then there holds
det
1≤i,j≤n

(n −2e) (
1
2
)
e
(m+e) (m+n −e) (
1
2
−n)
e
. (3.48)
In [50], this result was applied to enumerate all rhombus tilings of a symmetric
hexagon that contain a ﬁxed rhombus. In Section 4 of [50] there can be found several
conjectures about the enumeration of rhombus tilings with more than just one ﬁxed
rhombus, all of which amount to evaluating other mixtures of the above-mentioned two
determinants.
As last binomial determinants, I cannot resist to show the, so far, weirdest deter-
minant evaluations that I am aware of. They arose in an attempt [16] by Bombieri,
Hunt and van der Poorten to improve on Thue’s method of approximating an algebraic
number. In their paper, they conjectured the following determinant evaluation, which,
thanks to van der Poorten [132], has recently become a theorem (see the subsequent
paragraphs and, in particular, Theorem 51 and the remark following it).
Theorem 49. Let N and l be positive integers. Let M be the matrix with rows labelled
by pairs (i
1
, i
2
) with 0 ≤ i
1
≤ 2l(N −i
2
) −1 (the intuition is that the points (i
1
, i
2
) are
the lattice points in a right-angled triangle), with columns labelled by pairs (j
1
, j
2
) with
0 ≤ j
2
≤ N and 2l(N − j
2
) ≤ j
1
≤ l(3N − 2j
2
) − 1 (the intuition is that the points
(j
1
, j
2
) are the lattice points in a lozenge), and entry in row (i
1
, i
2
) and column (j
1
, j
2
)
equal to

j
1
i
1

j
2
i
2

.
Then the determinant of M is given by
±

¸
l−1
k=0
k!
¸
3l−1
k=2l
k!
¸
2l−1
k=l
k!

(
N+2
3
)
.
This determinant evaluation is just one in a whole series of conjectured determinant
evaluations and greatest common divisors of minors of a certain matrix, many of them
reported in [16]. These conjectures being settled, the authors of [16] expect important
implications in the approximation of algebraic numbers.
The case N = 1 of Theorem 49 is a special case of (3.11), and, thus, on a shallow
level. On the other hand, the next case, N = 2, is already on a considerably deeper
ADVANCED DETERMINANT CALCULUS 45
level. It was ﬁrst proved in [94], by establishing, in fact, a much more general result,
given in the next theorem. It reduces to the N = 2 case of Theorem 49 for x = 0,
b = 4l, and c = 2l. (In fact, the x = 0 case of Theorem 50 had already been conjectured
in [16].)
Theorem 50. Let b, c be nonnegative integers, c ≤ b, and let ∆(x; b, c) be the determi-
nant of the (b +c) (b +c) matrix

(b+i)/2−(b−c+i)/2
.
(3.50)
The proof of this result in [94] could be called “heavy”. It proceeded by “identiﬁcation
of factors”. Thus, it was only the introduction of the parameter x in the determinant in
(3.49) that allowed the attack on this special case of the conjecture of Bombieri, Hunt
and van der Poorten. However, the authors of [94] (this includes myself) failed to ﬁnd a
way to introduce a parameter into the determinant in Theorem 49 for generic N (that
is, in a way such the determinant would still factor nicely). This was accomplished by
van der Poorten [132]. He ﬁrst changed the entries in the determinant slightly, without
changing the value of the determinant, and then was able to introduce a parameter. I
state his result, [132, Sec. 5, Main Theorem], in the theorem below. For the proof of
his result he used “identiﬁcation of factors” as well, thereby considerably simplifying
and illuminating arguments from [94].
Theorem 51. Let N and l be positive integers. Let M be the matrix with rows labelled
by pairs (i
1
, i
2
) with 0 ≤ i
1
≤ 2l(N −i
2
) −1, with columns labelled by pairs (j
1
, j
2
) with
0 ≤ j
2
≤ N and 0 ≤ j
1
≤ lN −1, and entry in row (i
1
, i
2
) and column (j
1
, j
2
) equal to
(−1)
i
1
−j
1

∞
√
−1
−∞
√
−1
(2z)
2ν
cos πz
dz.
Slightly diﬀerent proofs of (3.52) can be found in [1] and [108, App. A.5], together
with more Hankel determinant evaluations (among which are also (3.56) and (3.58),
respectively). The evaluation (3.54) can be derived by considering Charlier polyno-
mials (see [35] for such a derivation in a special case). The evaluation (3.55) follows
from the fact that Hermite polynomials are moments of slightly shifted Hermite poly-
nomials, as explained in [71]. In fact, the papers [71] and [72] contain more examples
of orthogonal polynomials which are moments, thus in particular implying Hankel de-
terminant evaluations whose entries are Laguerre polynomials, Meixner polynomials,
and Al-Salam–Chihara polynomials. Hankel determinants where the entries are (clas-
sical) orthogonal polynomials are also considered in [77], where they are related to
Wronskians of orthogonal polynomials. In particular, there result Hankel determinant
evaluations with entries being Legendre, ultraspherical, and Laguerre polynomials [77,
(12.3), (14.3), (16.5), ' 28], respectively. The reader is also referred to [103], where
illuminating proofs of these identities between Hankel determinants and Wronskians
are given, by using the fact that Hankel determinants can be seen as certain Schur
functions of rectangular shape, and by applying a ‘master identity’ of Turnbull [178,
p. 48] on minors of a matrix. (The evaluations (3.52), (3.55) and (3.56) can be found in
[103] as well, as corollaries to more general results.) Alternative proofs of (3.52), (3.54)
and (3.55) can be found in [141], see also [139] and [140].
Clearly, to prove (3.56)–(3.58) one would proceed in the same way as in Section 2.7.
(Identity (3.58) is in fact the evaluation (2.38) that we derived in Section 2.7.) The
evaluations (3.59) and (3.60) are equivalent to (3.58), because the matrix underlying
the determinant in (3.58) has a checkerboard pattern (recall that Bernoulli numbers
with odd indices are zero, except for B
1
), and therefore decomposes into the prod-
uct of a determinant of the form (3.59) and a determinant of the form (3.60). Very
interestingly, variations of (3.56)–(3.60) arise as normalization constants in statistical
mechanics models, see e.g. [14, (4.36)], [32, (4.19)], and [108, App. A.5]. A common
generalization of (3.56)–(3.58) can be found in [51, Sec. 5]. Strangely enough, it was
needed there in the enumeration of rhombus tilings.
48 C. KRATTENTHALER
In view of Section 2.7, any continued fraction expansion of the form (2.30) gives rise
to a Hankel determinant evaluation. Thus, many more Hankel determinant evaluations
follow e.g. from work by Rogers [151], Stieltjes [171, 172], Flajolet [44], Han, Randri-
anarivony and Zeng [65, 64, 142, 143, 144, 145, 146, 201], Ismail, Masson and Valent
[70, 73] or Milne [113, 114, 115, 116], in particular, evaluations of Hankel determinant
featuring Euler numbers with odd indices (these are given through the generating func-
tion tan z =
¸
∞
k=0
E
2k+1
z
2k+1
/(2k +1)!), Genocchi numbers, q- and other extensions of
Catalan, Euler and Genocchi numbers, and coeﬃcients in the power series expansion
of Jacobi elliptic functions. Evaluations of the latter type played an important role in
Milne’s recent beautiful results [113, 114] on the number of representations of integers
as sums of m-th powers (see also [108, App. A.5]).
For further evaluations of Hankel determinants, which apparently do not follow from
known results about continued fractions or orthogonal polynomials, see [68, Prop. 14]
and [51, Sec. 4].
Next we state two charming applications of Lemma 16 (see [189]).
Theorem 53. Let x be a nonnegative integer. For any nonnegative integer n there
hold
det
0≤i,j≤n

(xi)!
(xi +j)!
S(xi +j, xi)

=

x
2

(
n+1
2
)
(3.61)
where S(m, k) is a Stirling number of the second kind (the number of partitions of an
m-element set into k blocks; cf. [166, p. 33]), and
det
0≤i,j≤n

(xi)!
(xi +j)!
s(xi +j, xi)

=

−
x
2

(
n+1
2
)
, (3.62)
where s(m, k) is a Stirling number of the ﬁrst kind (up to sign, the number of permu-
tations of m elements with exactly k cycles; cf. [166, p. 18]).
Theorem 54. Let A
ij
denote the number of representations of j as a sum of i squares
of nonnegative integers. Then det
0≤i,j≤n
(A
ij
) = 1 for any nonnegative integer n. The
same is true if “squares” is replaced by “cubes,” etc.
After having seen so many determinants where rows and columns are indexed by
integers, it is time for a change. There are quite a few interesting determinants whose
rows and columns are indexed by (other) combinatorial objects. (Actually, we already
encountered one in Conjecture 49.)
We start by a determinant where rows and columns are indexed by permutations.
Its beautiful evaluation was obtained at roughly the same time by Varchenko [184] and
Zagier [193].
Theorem 55. For any positive integer n there holds
det
σ,π∈Sn

q
inv(σπ
−1
)

=
n
¸
i=2
(1 −q
i(i−1)
)
(
n
i
)(i−2)! (n−i+1)!
, (3.63)
where S
n
denotes the symmetric group on n elements.
ADVANCED DETERMINANT CALCULUS 49
This determinant evaluation appears in [193] in the study of certain models in inﬁnite
statistics. However, as Varchenko et al. [20, 153, 184] show, this determinant evaluation
is in fact just a special instance in a whole series of determinant evaluations. The
latter papers give evaluations of determinants corresponding to certain bilinear forms
associated to hyperplane arrangements and matroids. Some of these bilinear forms
are relevant to the study of hypergeometric functions and the representation theory
of quantum groups (see also [185]). In particular, these results contain analogues of
(3.63) for all ﬁnite Coxeter groups as special cases. For other developments related to
Theorem 55 (and diﬀerent proofs) see [36, 37, 40, 67], tying the subject also to the
representation theory of the symmetric group, to noncommutative symmetric functions,
and to free Lie algebras, and [109]. For more remarkable determinant evaluations related
to hyperplane arrangements see [39, 182, 183]. For more determinant evaluations related
to hypergeometric functions and quantum groups and algebras, see [175, 176], where
determinants arising in the context of Knizhnik-Zamolodchikov equations are computed.
The results in [20, 153] may be considered as a generalization of the Shapovalov de-
terminant evaluation [159], associated to the Shapovalov form in Lie theory. The latter
has since been extended to Kac–Moody algebras (although not yet in full generality),
see [31].
There is a result similar to Theorem 55 for another prominent permutation statistics,
MacMahon’s major index. (The major index maj(π) is deﬁned as the sum of all positions
of descents in the permutation π, see e.g. [46].)
Theorem 56. For any positive integer n there holds
det
σ,π∈Sn

q
maj(σπ
−1
)

=
n
¸
i=2
(1 −q
i
)
n! (i−1)/i
. (3.64)
As Jean–Yves Thibon explained to me, this determinant evaluation follows from
results about the descent algebra of the symmetric group given in [95], presented
there in an equivalent form, in terms of noncommutative symmetric functions. For
the details of Thibon’s argument see Appendix C. Also the bivariate determinant
det
σ,π∈Sn

x
des(σπ
−1
)
q
maj(σπ
−1
)

seems to possess an interesting factorization.
The next set of determinant evaluations shows determinants where the rows and
columns are indexed by set partitions. In what follows, the set of all partitions of
¦1, 2, . . . , n¦ is denoted by Π
n
. The number of blocks of a partition π is denoted by
bk(π). A partition π is called noncrossing, if there do not exist i < j < k < l such
that both i and k belong to one block, B
1
say, while both j and l belong to another
block which is diﬀerent from B
1
. The set of all noncrossing partitions of ¦1, 2, . . . , n¦
is denoted by NC
n
. (For more information about noncrossing partitions see [160].)
Further, poset-theoretic, notations which are needed in the following theorem are:
Given a poset P, the join of two elements x and y in P is denoted by x ∨
P
y, while
the meet of x and y is denoted by x ∧
P
y. The characteristic polynomial of a poset
P is written as χ
P
(q) (that is, if the maximum element of P has rank h and µ is the
M¨ obius function of P, then χ
P
(q) :=
¸
p∈P
µ(
ˆ
0, p)q
h−rank(p)
, where
ˆ
0 stands for the
minimal element of P). The symbol ˜ χ
P
(q) denotes the reciprocal polynomial q
h
χ
P
(1/q)
of χ
P
(q). Finally, P
∗
is the order-dual of P.
50 C. KRATTENTHALER
Theorem 57. Let n be a positive integer. Then
det
π,γ∈Πn

q
bk(π∧
Πn
γ)

=
n
¸
i=1

q ˜ χ
Π
∗
i
(q)

(
n
i
)B(n−i)
, (3.65)
where B(k) denotes the k-th Bell number (the total number of partitions of a k-element
set; cf. [166, p. 33]). Furthermore,
det
π,γ∈Πn

q
bk(π∨
Πn
γ)

=
n
¸
i=1

q χ
Π
i
(q)

S(n,i)
, (3.66)
where S(m, k) is a Stirling number of the second kind (the number of partitions of an
m-element set into k blocks; cf. [166, p. 33]). Next,
det
π,γ∈NCn

q
bk(π∧
NCn
γ)

= q
(
2n−1
n
)
n
¸
i=1

˜ χ
NC
i
(q)

(
2n−1−i
n−1
)
, (3.67)
and
det
π,γ∈NCn

q
bk(π∨
NCn
γ)

= q
1
n+1
(
2n
n
)
n
¸
i=1

χ
NC
i
(q)

(
2n−1−i
n−1
)
, (3.68)
Finally,
det
π,γ∈NCn

q
bk(π∨
Πn
γ)

= q
(
2n−1
n
)
n−1
¸
i=1

U
i+1
(
√
q/2)
qU
i−1
(
√
q/2)

i+1
n
(
2n
n−1−i
)
, (3.69)
where U
m
(x) :=
¸
j≥0
(−1)
j

m−j
j

(2x)
m−2j
is the m-th Chebyshev polynomials of the
second kind.
The evaluations (3.65)–(3.68) are due to Jackson [75]. The last determinant eval-
uation, (3.69), is the hardest among those. It was proved independently by Dahab
[33] and Tutte [181]. All these determinants are related to the so-called Birkhoﬀ–Lewis
equation from chromatic graph theory (see [33, 180] for more information).
A determinant of somewhat similar type appeared in work by Lickorish [104] on 3-
manifold invariants. Let NCmatch(2n) denote the set of all noncrossing perfect match-
ings of 2n elements. Equivalently, NCmatch(2n) can be considered as the set of all
noncrossing partitions of 2n elements with all blocks containing exactly 2 elements.
Lickorish considered a bilinear form on the linear space spanned by NCmatch(2n). The
corresponding determinant was evaluated by Ko and Smolinsky [80] using an elegant
recursive approach, and independently by Di Francesco [47], whose calculations are
done within the framework of the Temperley–Lieb algebra (see also [49]).
Theorem 58. For α, β ∈ NCmatch(2n), let c(α, β) denote the number of connected
components when the matchings α and β are superimposed. Equivalently, c(α, β) =
bk(α ∨
Π
2n
β). For any positive integer n there holds
det
α,β∈NCmatch(2n)

q
c(α,β)

=
n
¸
i=1
U
i
(q/2)
a
2n,2i
, (3.70)
where U
m
(q) is the Chebyshev polynomial of the second kind as given in Theorem 57,
and where a
2n,2i
= c
2n,2i
−c
2n,2i+2
with c
n,h
=

n
(n−h)/2

−

n
(n−h)/2−1

.
ADVANCED DETERMINANT CALCULUS 51
Di Francesco [47, Theorem 2] does also provide a generalization to partial matchings,
and in [48] a generalization in an SU(n) setting, the previously mentioned results being
situated in the SU(2) setting. While the derivations in [47] are mostly combinatorial,
the derivations in [48] are based on computations in quotients of type A Hecke algebras.
There is also an interesting determinant evaluation, which comes to my mind, where
rows and columns of the determinant are indexed by integer partitions. It is a result
due to Reinhart [147]. Interestingly, it arose in the analysis of algebraic diﬀerential
equations.
In concluding, let me attract your attention to other determinant evaluations which
I like, but which would take too much space to state and introduce properly.
For example, there is a determinant evaluation, conjectured by Good, and proved by
Goulden and Jackson [60], which arose in the calculation of cumulants of a statistic anal-
ogous to Pearson’s chi-squared for a multinomial sample. Their method of derivation
is very combinatorial, in particular making use of generalized ballot sequences.
Determinants arising from certain raising operators of sl(2)-representations are pre-
sented in [136]. As special cases, there result beautiful determinant evaluations where
rows and columns are indexed by integer partitions and the entries are numbers of
standard Young tableaux of skew shapes.
In [84, p. 4] (see also [192]), an interesting mixture of linear algebra and combinatorial
matrix theory yields, as a by-product, the evaluation of the determinant of certain
incidence mappings. There, rows and columns of the relevant matrix are indexed by all
subsets of an n-element set of a ﬁxed size.
As a by-product of the analysis of an interesting matrix in quantum information
theory [93, Theorem 6], the evaluation of a determinant of a matrix whose rows and
columns are indexed by all subsets of an n-element set is obtained.
Determinant evaluations of q-hypergeometric functions are used in [177] to compute
q-Selberg integrals.
And last, but not least, let me once more mention the remarkable determinant evalu-
ation, arising in connection with holonomic q-diﬀerence equations, due to Aomoto and
Kato [11, Theorem 3], who thus proved a conjecture by Mimachi [118].
Appendix A: A word about guessing
The problem of guessing a formula for the generic element a
n
of a sequence (a
n
)
n≥0
out of the ﬁrst few elements was present at many places, in particular this is crucial for
a successful application of the “identiﬁcation of factors” method (see Section 2.4) or of
LU-factorization (see Section 2.6). Therefore some elaboration on guessing is in order.
First of all, as I already claimed, guessing can be largely automatized. This is due to
the following tools
11
:
1. Superseeker, the electronic version of the “Encyclopedia of Integer Sequences”
[162, 161] by Neil Sloane and Simon Plouﬀe (see Footnote 2 in the Introduction),
11
In addition, one has to mention Frank Garvan’s qseries [54], which is designed for guessing and
computing within the territory of q-series, q-products, eta and theta functions, and the like. Procedures
like prodmake or qfactor, however, might also be helpful for the evaluation of “q-determinants”. The
package is available from http://www.math.ufl.edu/~frank/qmaple.html.
52 C. KRATTENTHALER
2. gfun by Bruno Salvy and Paul Zimmermann and Mgfun by Frederic Chyzak (see
Footnote 3 in the Introduction),
3. Rate by the author (see Footnote 4 in the Introduction).
If you send the ﬁrst few elements of your sequence to Superseeker then, if it overlaps
with a sequence that is stored there, you will receive information about your sequence
such as where your sequence already appeared in the literature, a formula, generating
function, or a recurrence for your sequence.
The Maple package gfun provides tools for ﬁnding a generating function and/or a
recurrence for your sequence. (In fact, Superseeker does also automatically invoke
features from gfun.) Mgfun does the same in a multidimensional setting.
Within the “hypergeometric paradigm,” the most useful is the Mathematica pro-
gram Rate (“Rate!” is German for “Guess!”), respectively its Maple equivalent GUESS.
Roughly speaking, it allows to automatically guess “closed forms”.
12
The program
is based on the observation that any “closed form” sequence (a
n
)
n≥0
that appears
within the “hypergeometric paradigm” is either given by a rational expression, like
a
n
= n/(n+1), or the sequence of successive quotients (a
n+1
/a
n
)
n≥0
is given by a ratio-
nal expression, like in the case of central binomial coeﬃcients a
n
=

2n
n

, or the sequence
of successive quotients of successive quotients ((a
n+2
/a
n+1
)/(a
n+1
/a
n
))
n≥0
is given by
a rational expression, like in the case of the famous sequence of numbers of alternating
sign matrices (cf. the paragraphs following (3.9), and [18, 19, 111, 148, 97, 198, 199]
for information on alternating sign matrices),
a
n
=
n−1
¸
i=0
(3i + 1)!
(n +i)!
, (A.1)
etc. Given enough special values, a rational expression is easily found by rational
interpolation.
This is implemented in Rate. Given the ﬁrst m terms of a sequence, it takes the
ﬁrst m − 1 terms and applies rational interpolation to these, then it applies rational
interpolation to the successive quotients of these m − 1 terms, etc. For each of the
obtained results it is tested if it does also give the m-th term correctly. If it does, then
the corresponding result is added to the output, if it does not, then nothing is added
to the output.
Here is a short demonstration of the Mathematica program Rate. The output shows
guesses for the i0-th element of the sequence.
In[1]:= ''rate.m
In[2]:= Rate[1,2,3]
Out[2]= ¦i0¦
In[3]:= Rate[2/3,3/4,4/5,5/6]
12
Commonly, by “closed form” (“NICE” in Zeilberger’s “terminology”) one means an expression
which is built by forming products and quotients of factorials. A strong indication that you encounter
a sequence (a
n
)
n≥0
for which a “closed form” exists is that the prime factors in the prime factorization
of a
n
do not grow rapidly as n becomes larger. (In fact, they should grow linearly.)
ADVANCED DETERMINANT CALCULUS 53
1 + i0
Out[3]= ¦------¦
2 + i0
Now we try the central binomial coeﬃcients:
In[4]:= Rate[1,2,6,20,70]
-1 + i0
2 (-1 + 2 i1)
Out[4]= ¦ -------------¦
i1
i1=1
It needs the ﬁrst 8 values to guess the formula (A.1) for the numbers of alternating sign
matrices:
In[5]:= Rate[1,2,7,42,429,7436,218348,10850216]
-1 + i0 -1 + i1
3 (2 + 3 i2) (4 + 3 i2)
Out[5]= ¦ 2 ( -----------------------)¦
4 (1 + 2 i2) (3 + 2 i2)
i1=1 i2=1
However, what if we encounter a sequence where all these nice automatic tools fail?
Here are a few hints. First of all, it is not uncommon to encounter a sequence (a
n
)
n≥0
which has actually a split deﬁnition. For example, it may be the case that the subse-
quence (a
2n
)
n≥0
of even-numbered terms follows a “nice” formula, and that the subse-
quence (a
2n+1
)
n≥0
of odd-numbered terms follows as well a “nice,” but diﬀerent, formula.
Then Rate will fail on any number of ﬁrst terms of (a
n
)
n≥0
, while it will give you some-
thing for suﬃciently many ﬁrst terms of (a
2n
)
n≥0
, and it will give you something else
for suﬃciently many ﬁrst terms of (a
2n+1
)
n≥0
.
Most of the subsequent hints apply to a situation where you encounter a sequence
p
0
(x), p
1
(x), p
2
(x), . . . of polynomials p
n
(x) in x for which you want to ﬁnd (i.e., guess)
a formula. This is indeed the situation in which you are generally during the guessing
for “identiﬁcation of factors,” and also usually when you perform a guessing where a
parameter is involved.
To make things concrete, let us suppose that the ﬁrst 10 elements of your sequence
of polynomials are
54 C. KRATTENTHALER
1, 1 +2x, 1 +x +3x
2
,
1
6

, . . . (A.2)
You may of course try to guess the coeﬃcients of powers of x in these polynomials.
But within the “hypergeometric paradigm” this does usually not work. In particular,
that does not work with the above sequence of polynomials.
A ﬁrst very useful idea is to guess through interpolation. (For example, this is what
helped to guess coeﬃcients in [43].) What this means is that, for each p
n
(x) you try to
ﬁnd enough values of x for which p
n
(x) appears to be “nice” (the prime factorization
of p
n
(x) has small prime factors, see Footnote 12). Then you guess these special eval-
uations of p
n
(x) (by, possibly, using Rate or GUESS), and, by interpolation, are able to
write down a guess for p
n
(x) itself.
Let us see how this works for our sequence (A.2). A few experiments will convince
you that p
n
(x), 0 ≤ n ≤ 9 (this is all we have), appears to be “nice” for x = 0, 1, . . . , n.
Furthermore, using Rate or GUESS, you will quickly ﬁnd that, apparently, p
n
(e) =

2n+e
e

for e = 0, 1, . . . , n. Therefore, as it also appears to be the case that p
n
(x) is of degree
n, our sequence of polynomials should be given (using Lagrange interpolation) by
p
n
(x) =
n
¸
e=0

2n +e
e

x(x −1) (x −e + 1)(x −e −1) (x −n)
e(e −1) 1 (−1) (e −n)
. (A.3)
Another useful idea is to try to expand your polynomials with respect to a “suitable”
basis. (For example, this is what helped to guess coeﬃcients in [30] or [94, e.g., (3.15),
(3.33)].) Now, of course, you do not know beforehand what “suitable” could be in your
situation. Within the “hypergeometric paradigm” candidates for a suitable basis are
always more or less sophisticated shifted factorials. So, let us suppose that we know
that we were working within the “hypergeometric paradigm” when we came across
the example (A.2). Then the simplest possible bases are (x)
k
, k = 0, 1, . . . , or (−x)
k
,
k = 0, 1, . . . . It is just a matter of taste, which of these to try ﬁrst. Let us try the
latter. Here are the expansions of p
3
(x) and p
4
(x) in terms of this basis (actually, in
terms of the equivalent basis

x
k

, k = 0, 1, . . . ):
1
6

6 + 31x −15x
2
+ 20x
3

= 1 + 6

x
1

+ 15

x
2

+ 20

x
3

,
1
12

12 −58x + 217x
2
−98x
3
+ 35x
4

= 1 + 8

x
1

+ 28

x
2

+ 56

x
3

+ 70

x
4

.
I do not know how you feel. For me this is enough to guess that, apparently,
p
n
(x) =
n
¸
k=0

2n
k

x
k

.
ADVANCED DETERMINANT CALCULUS 55
(Although this is not the same expression as in (A.3), it is identical by means of a
3
F
2
-transformation due to Thomae, see [55, (3.1.1)]).
As was said before, we do not know beforehand what a “suitable” basis is. Therefore
you are advised to get as much a priori information about your polynomials as possible.
For example, in [28] it was “known” to the authors that the result which they wanted
to guess (before being able to think about a proof) is of the form (NICE PRODUCT)
(IRREDUCIBLE POLYNOMIAL). (I.e., experiments indicated that.) Moreover, they
knew that their (IRREDUCIBLE POLYNOMIAL), a polynomial in m, p
n
(m) say,
would have the property p
n
(−m − 2n + 1) = p
n
(m). Now, if we are asking ourselves
what a “suitable” basis could be that has this property as well, and which is built
in the way of shifted factorials, then the most obvious candidate is (m + n − k)
2k
=
(m+n −k)(m+n −k + 1) (m+n +k −1), k = 0, 1, . . . . Indeed, it was very easy
to guess the expansion coeﬃcients with respect to this basis. (See Theorems 1 and 2
in [28]. The polynomials that I was talking about are represented by the expression in
big parentheses in [28, (1.2)].)
If the above ideas do not help, then I have nothing else to oﬀer than to try some,
more or less arbitrary, manipulations. To illustrate what I could possibly mean, let us
again consider an example. In the course of working on [90], I had to guess the result
of a determinant evaluation (which became Theorem 8 in [90]; it is reproduced here
as Theorem 43). Again, the diﬃcult part of guessing was to guess the “ugly” part of
the result. As the dimension of the determinant varied, this gave a certain sequence
p
n
(x, y) of polynomials in two variables, x and y, of which I display p
4
(x, y):
In[1]:= VPol[4]
2 3 4 2 3 2
Out[1]= 6 x + 11 x + 6 x + x + 6 y - 10 x y - 6 x y - 4 x y + 11 y -
2 2 2 3 3 4
> 6 x y + 6 x y + 6 y - 4 x y + y
(What I subsequently describe is the actual way in which the expression for p
n
(x, y) in
terms of the sum on the right-hand side of (3.38) was found.) What caught my eyes was
the part of the polynomial independent of y, x
4
+ 6x
3
+ 11x
2
+ 6x, which I recognized
as (x)
4
= x(x + 1)(x + 2)(x + 3). For the fun of it, I subtracted that, just to see what
would happen:
In[2]:= Factor[%-x(x+1)(x+2)(x+3)]
2 3 2 2 2
Out[2]= y (6 - 10 x - 6 x - 4 x + 11 y - 6 x y + 6 x y + 6 y - 4 x y +
3
` y )
Of course, a y factors. Okay, let us cancel that:
In[3]:= %/y
2 3 2 2 2 3
Out[3]= 6 - 10 x - 6 x - 4 x + 11 y - 6 x y + 6 x y + 6 y - 4 x y + y
56 C. KRATTENTHALER
One day I had the idea to continue in a “systematic” manner: Let us subtract/add an
appropriate multiple of (x)
3
! Perhaps, “appropriate” in this context is to add 4(x)
3
,
because that does at least cancel the third powers of x:
In[4]:= Factor[%+4x(x+1)(x+2)]
2 2
Out[4]= (1 + y) (6 - 2 x + 6 x + 5 y - 4 x y + y )
I assume that I do not have to comment the rest:
In[5]:= %/(y+1)
2
Out[5]= 6 - 2 x + 6 x + 5 y - 4 x y + y
In[6]:= Factor[%-6x(x+1)]
Out[6]= (2 + y) (3 - 4 x + y)
In[7]:= %/(y+2)
Out[7]= 3 - 4 x + y
In[8]:= Factor[%+4x]
Out[8]= 3 + y
What this shows is that
p
4
(x, y) = (x)
4
−4(x)
3
(y)
1
+ 6(x)
2
(y)
2
−4(x)
1
(y)
3
+ (y)
4
.
No doubt that, at this point, you would have immediately guessed (as I did) that, in
general, we “must” have (compare (3.38))
p
n
(x, y) =
n
¸
k=0
(−1)
k

n
k

(x)
k
(y)
n−k
.
Appendix B: Turnbull’s polarization of Bazin’s theorem implies most of the
identities in Section 2.2
In this appendix we show that all the determinant lemmas from Section 2.2, with the
exception of Lemmas 8 and 9, follow from the evaluation of a certain determinant of
minors of a given matrix, an observation which I owe to Alain Lascoux. This evaluation,
due to Turnbull [179, p. 505], is a polarized version of a theorem of Bazin [119, II,
pp. 206–208] (see also [102, Sec. 3.1 and 3.4]).
For the statement of Turnbull’s theorem we have to ﬁx an n-rowed matrix A, in which
we label the columns, slightly unconventionally, by a
2
, . . . , a
m
, b
21
, b
31
, b
32
, b
41
, . . . , b
n,n−1
,
x
1
, x
2
, . . . , x
n
, for some m ≥ n, i.e., A is an n (n +m−1 +